docs/GLOSSARY.md

Glossary — deep reference

Purpose. This is the vocabulary the rest of the documentation uses, defined precisely, with the file that defines or enforces each term. A term is only listed if it appears in the repository; a definition is only given if a file supports it.

Status tags used on every substantive claim: IMPLEMENTED · VERIFIED · MEASURED · ATTEMPTED · NOT RUN · BLOCKED · DEFERRED · REJECTED · OPEN · RESOLVED · CLOSED.

The single most important rule in this document: do not fabricate. Every definition is grounded in a file that was read, and the file is named. Where a term's meaning is not established, this document writes UNKNOWN — not established from the available evidence rather than inventing one.

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How to use this glossary

The entries are grouped so that a reader can read one group at a time:

Part	Group	Jump
A	Tasks	§A
B	Models and methods	§B
C	Metrics and losses	§C
D	Data and geospatial	§D
E	System concepts	§E
F	Deployment and operations	§F
G	Status vocabulary	§G
H	Index — where each term is defined or enforced	§H

Each entry gives a precise definition, the file that defines or enforces it, and, where relevant, a cross-link to the deep chapter that covers it in full.

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Part A — Tasks

The task vocabulary is a closed set with exactly seven members, defined once in core/schemas.py (class Task(str, Enum)) and mirrored in the router's label space (router/label_space.py::TASK_CLASSES). The three are not interchangeable and the planner must not substitute one for another — core/schemas.py:41-46 says so in a comment on CHANGE_VQA.

Term	Definition	Defined / enforced
vqa	Visual question answering about one asset: a natural-language question in, a short answer out. Runs through the SmolVLM specialist.	core/schemas.py:36 (Task.VQA); router/label_space.py; architecture/05-specialists.md §7
caption	A prose description of one asset. Same specialist as vqa, a different prompt kind.	core/schemas.py:37; architecture/05-specialists.md §10
grounding	Localisation: a text phrase in, boxes out. RemoteCLIP encoder + a trained head. The VLM is never the source of coordinates (finding C-5).	core/schemas.py:38; architecture/05-specialists.md §11–§17
change	The change detector: two temporally corresponding assets in, a spatial change map out, no language output. STANet-style Siamese network.	core/schemas.py:39; architecture/05-specialists.md §18–§26
change_vqa	R-02. Two temporally corresponding assets plus a change-oriented question in, a short answer out. Distinct from change (which returns a map, no language) and from vqa (which answers about one asset).	core/schemas.py:41-46; architecture/05-specialists.md §27–§32
optical_sar	Fusion of an optical and a SAR asset: CROMA encoder + a trained fusion head over 19 classes. Requires two modalities.	core/schemas.py:40; configs/base.yaml (croma, fusion); architecture/05-specialists.md §33+
unsupported	The refusal class. Returned when nothing matches — never a guess. The lexical fallback returns unsupported with low confidence rather than inventing a task (router/fallback.py).	core/schemas.py:47; router/fallback.py; enforced by core/config.py:198-206 (the task list must include unsupported)

Related task facts.

• temporal / spatial_output / language_output are three binary heads, not tasks. TEMPORAL_TASKS = {"change"}, SPATIAL_TASKS = {"grounding"}, DUAL_MODALITY_TASKS = {"optical_sar"} (router/label_space.py). Intent._consistency (core/schemas.py:107-114) forces temporal = True for change/change_vqa and modality = optical_sar for optical_sar.
• Asset-count contract. change, change_vqa and optical_sar require exactly 2 assets; vqa and caption require exactly 1 (MODELS.md §3.0.3). The authoritative source is core/planner.CAPABILITY_ASSETS and SpecialistSpec.requires_assets (docs/DEPLOYMENT_ARCHITECTURE.md §2.2).

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Part B — Models and methods

B.1 Backbones (frozen)

Term	Definition	Defined / enforced
RemoteCLIP	The frozen vision-language encoder used by grounding. Repository chendelong/RemoteCLIP, file RemoteCLIP-ViT-B-32.pt, revision bf1d8a3ccf2d. A CLIP ViT-B/32 adapted to remote sensing. Measured contract: patch size 32, transformer width 768, projected dim 512 (visual.proj is (768, 512)), 151,277,313 parameters. At 224 px it yields a 7×7 = 49-token grid.	configs/base.yaml (grounding); specialists/grounding/remoteclip.py (_verify_contract); MODELS.md §2.3
CROMA	The frozen multi-modal encoder used by optical_sar. Repository antofuller/CROMA, file CROMA_base.pt, revision 0dd28e3d633b. encoder_dim 768, image_resolution 120 (15×15 = 225 patches), optical_channels 12, sar_channels 2. Asymmetric (s1_depth=6, s2_depth=12), with directional joint cross-attention (SAR queries optical). 194,365,440 parameters.	configs/base.yaml (croma); specialists/optical_sar/; MODELS.md §2.4
SmolVLM	The frozen vision-language model used by vqa and caption. HuggingFaceTB/SmolVLM-500M-Instruct, revision a7da5b986cb5, ~1015 MB safetensors, 516,165,824 base parameters.	configs/base.yaml (vlm); MODELS.md §2.2
MiniLM	The frozen sentence encoder under the intent router. sentence-transformers/all-MiniLM-L6-v2, revision 1110a243fdf4, 90.9 MB, 22,713,216 parameters, 384-dim embeddings. Tokenizer ceiling 256; truncation set to 128.	configs/base.yaml (router); router/encoder.py; architecture/04-router.md §2
STANet	The architecture family the change detector follows: a Siamese network with spatial-temporal attention over feature differences and a progressive decoder. Reimplemented rather than vendored (the upstream is Python 3.6-era and depends on visdom/apex). Weights are tied, not copied: both branches call the same SharedResNetEncoder instance.	specialists/change/stanet.py; architecture/05-specialists.md §18

B.2 Attention modules

Term	Definition	Defined / enforced
PAM	The spatial attention module used in the change detector (STANet's spatial path). SpatialAttention is PAM-style: 1×1 query/key convolutions to hidden = max(1, channels // reduction) with reduction = 8, a 1×1 value, a 1×1 out, softmax(q @ k / sqrt(hidden)) over positions, and a residual. configs/base.yaml sets change.sa_mode: PAM.	specialists/change/stanet.py; architecture/05-specialists.md §18.4
BAM	The alternative attention mode in the same sa_mode enum (`BAM	PAM`). It raises — the constructor accepts the value only to fail visibly rather than silently aliasing PAM.

B.3 Adaptation and calibration methods

Term	Definition	Defined / enforced
LoRA	Low-Rank Adaptation: a small trainable delta attached to a frozen model's weight matrices. Here it is applied to the SmolVLM text-model projections with r=16, alpha=32, dropout=0.05. 8,683,520 trainable parameters on a 516,165,824-parameter base (1.68%).	configs/base.yaml (training.lora_*); artifacts/vlm/phase6_closure.json; MODELS.md §3.6
PEFT	Parameter-Efficient Fine-Tuning — the library/technique family that provides the LoRA adapter. The adapter is loaded with peft.PeftModel.from_pretrained(model, dir).	specialists/vqa/model.py::_attach_adapter; MODELS.md §1.1
Temperature scaling	A post-hoc calibration that divides logits by a single fitted scalar T before the sigmoid, to bring predicted probabilities closer to observed frequencies. Here T = 0.9772731820958189, fitted on Val with n_samples = 16441.	evidence/engine.py (calibrate, confidence_for); artifacts/calibration_v001.json; architecture/06-evidence-and-confidence.md §8
_is_effective	The rule that a "fitted" T of exactly 1.0 is reported as uncalibrated — a T of 1.0 applies no transform, so claiming calibration would be a lie.	evidence/engine.py; architecture/06-evidence-and-confidence.md §8.3

The calibration result is a measured negative. ECE went 0.013755 → 0.014929 (worse). The calibration path is retained only because it is in the frozen config, not because it helped (DOCS_STYLE_GUIDE.md §3; LIMITATIONS.md item 7). The path is live (core/controller.py:218 loads it; evidence/engine.py:643 applies it).

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Part C — Metrics and losses

Metric implementations live in the evaluation layer; the honesty rules that govern how they are reported are in EVALUATION.md §2 (the eight rules) and architecture/06-evidence-and-confidence.md.

Term	Definition	Where it is used / defined
IoU	Intersection over Union — `	A ∩ B
mIoU	Mean IoU across classes — the macro IoU. For binary change this is the mean of the two class IoUs (change / no-change).	artifacts/change/eval_test/eval_result.json → metrics.macro.miou = 0.8457, metrics.pooled.miou = 0.9007
Macro vs pooled	Pooled aggregates all pixels (or all items) into one confusion matrix before computing the metric; macro computes the metric per class and averages, giving every class equal weight regardless of frequency. On an imbalanced corpus the two differ sharply, and both must be reported — never one alone.	DATASETS.md §3.5; EVALUATION.md Rule 4
F1	Harmonic mean of precision and recall. Reported both pooled (0.8964) and macro (0.7962) for change.	artifacts/change/eval_test/eval_result.json
exact_match	The fraction of VLM answers that match the reference answer exactly after normalisation. The VLM adapter's primary endpoint: 0.963 on 1,000 adapted-test questions.	architecture/05-specialists.md §9.3
recall@k	The fraction of items where a correct prediction is found within the top k (here a threshold ladder: Recall@0.10, @0.25, @0.50 for grounding IoU).	artifacts/grounding/.../eval_result_canonical.json → results.head_threshold.recall; architecture/05-specialists.md §16.5
BCE	Binary cross-entropy. Used for the router's three binary heads (via BCEWithLogitsLoss) and for the change detector's change/no-change logit. The logits form is mandatory — see below.	router/label_space.py; specialists/change/; architecture/05-specialists.md §29
Dice	A region-overlap loss closely related to IoU, used alongside BCE for change (bce_weight: 0.5, dice_weight: 0.5). "Why Dice is not decoration" — it counteracts the class imbalance BCE alone handles poorly.	configs/base.yaml (change.bce_weight, change.dice_weight); architecture/05-specialists.md §20.1
GIoU	Generalised IoU — IoU extended with a penalty for non-overlapping boxes, giving a gradient even when boxes do not intersect. The grounding head's box loss uses box_loss_weight: 0.5, giou_loss_weight: 0.3, confidence_loss_weight: 0.2.	configs/base.yaml (grounding_training); architecture/05-specialists.md §13.6
NMS	Non-Maximum Suppression — merges overlapping candidate boxes, keeping the highest-scoring and suppressing the rest above an IoU threshold. Grounding uses nms_iou: 0.50, max_candidates: 20, implemented in pure torch.	configs/base.yaml (grounding.nms_iou); specialists/grounding/; architecture/05-specialists.md §13.5
ECE	Expected Calibration Error — the average gap between predicted confidence and observed accuracy across confidence bins. Here it went 0.013755 → 0.014929 after temperature scaling (worse).	artifacts/calibration_v001.json; architecture/06-evidence-and-confidence.md §8; LIMITATIONS.md item 7
NLL	Negative log-likelihood — a proper scoring rule; improved marginally under calibration even though ECE worsened.	artifacts/calibration_v001.json; docs/API_CONTRACT.md §4

The saturated-BCE defect. A BCE implemented from sigmoid-then-BCE divides the backward pass by p(1−p), which explodes as p → 0 or 1. BCEWithLogits cannot saturate. The change-VQA head therefore uses the logits form, and the fallback is constrained accordingly (architecture/05-specialists.md §29).

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Part D — Data and geospatial

Dataset details are in DATASETS.md; leakage controls are in EVALUATION.md §3.2–§3.3 and configs/base.yaml (evaluation.leakage_split_key: scene_id).

D.1 Datasets

Term	Definition	Defined / used
LEVIR-CD-256	The change-detection corpus: 256×256 bitemporal building-change tiles. Split train: 7120, val: 1024, test: 2048 (configs/base.yaml → change.levir_split). The test split is the only headline that carries the VERIFIED tag.	DATASETS.md §3; artifacts/change/eval_test/eval_result.json
VRSBench	The grounding corpus. Boxes are stored normalised to 0–100; the project stores 0–1, hence grounding.benchmark_box_scale: 100.0. Train split has noisy ground truths (recorded). The eval used 16,159 records.	DATASETS.md §5; configs/base.yaml (grounding)
CDVQA	One of the two change-VQA corpora (question/answer annotations).	DATASETS.md §4
SECOND	The other change-VQA corpus. Measured finding: Test and Test2 are the same 968 images; and change_ratio_types has a different vocabulary in every split. The imagery is not shipped with the annotations.	DATASETS.md §4.2–§4.7
BigEarthNet	The optical-SAR fusion corpus (the reBEN v2 corpus) and a separate VLM-adaptation subset. 19 CLC classes. The local subset is 100 % single-label, unlike the official 1–11 multi-label scheme, so its metrics are not comparable to published numbers.	DATASETS.md §6; configs/base.yaml (fusion.num_classes: 19)
CLC classes	The CORINE Land Cover class taxonomy — the 19 label classes the fusion head predicts. Measured limitation: the live service returns class_18, a bare class index, not a human-readable CLC label.	configs/base.yaml (fusion.num_classes: 19); LIMITATIONS.md item 6
Single-label vs multi-label	A sample is single-label if it carries exactly one class; multi-label if it carries several. BigEarthNet's official scheme is multi-label (1–11); the project's local subset is single-label, which is why the fusion metrics must not be compared to published BigEarthNet numbers.	DATASETS.md §6.4; LIMITATIONS.md item 21

D.2 Geospatial and raster terms

Term	Definition	Defined / enforced
GeoTIFF	A TIFF raster carrying georeferencing (a CRS, an affine transform, bounds). The project reads rasters via preprocessing/raster.py and records geo metadata in GeoMetadata (core/schemas.py:120).	preprocessing/raster.py; core/schemas.py (GeoMetadata)
Band	One channel of a raster (e.g. a single spectral band). SensorDescriptor.available_bands / band_map describe which bands a sensor provides. CROMA expects exactly 12 optical and 2 SAR channels.	core/schemas.py:138 (SensorDescriptor); configs/base.yaml (croma.optical_channels: 12, sar_channels: 2)
Optical	Imagery in the visible/near-infrared; the default modality. CROMA expects 12 optical channels; the project normalises with a percentile stretch.	configs/base.yaml (optical); core/schemas.py (Modality.OPTICAL)
SAR	Synthetic Aperture Radar imagery. CROMA expects 2 SAR channels (VV, VH). Represented in dB.	configs/base.yaml (sar); core/schemas.py (Modality.SAR)
dB	Decibels — the logarithmic representation of SAR backscatter. configs/base.yaml sets sar.representation: db, clip_min_db: -30, clip_max_db: 5.	configs/base.yaml (sar)
Percentile stretch	A deterministic contrast stretch mapping the 2nd and 98th percentiles of finite values to the display range. "The same file always yields the same" result. Implemented in preprocessing/imagery.py.	preprocessing/imagery.py:63; configs/base.yaml (optical.normalization: percentile, lower_percentile: 2, upper_percentile: 98)
scene_id	A stable identifier for the acquisition scene a sample came from. It is the leakage split key: evaluation.leakage_split_key: scene_id. Splits are made by scene, never by example.	configs/base.yaml (evaluation); core/schemas.py:163 (AssetMetadata.scene_id); DATASETS.md §7.3
Split	A named partition of a corpus (train / val / test). Recorded on AssetMetadata.split. The public test split is immutable (evaluation.immutable_public_test: true).	configs/base.yaml (evaluation); core/schemas.py:165
Leakage	Any way that information from a test sample influences training, inflating measured performance. The project enforces five leakage rules in code and splits by group (template / hard-negative family), placing hard-negative families in test so their accuracy measures generalisation, not memorisation (finding F4-3).	configs/base.yaml (evaluation.leakage_split_key, router.training.hard_negatives_to_test: true); DATASETS.md §7.2; EVALUATION.md §3.2

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Part E — System concepts

E.1 The pipeline's nouns

Term	Definition	Defined / enforced
Router	The first of two stages: it reads the query and emits an Intent. It is advisory only — "Output of the learned router. Advisory only — the controller decides." (core/schemas.py:95). Implemented as a frozen MiniLM encoder plus a five-head adapter.	router/; core/schemas.py (Intent); architecture/04-router.md
Controller	The second stage and the decision authority. It parses, validates, plans, dispatches specialists, aggregates, verifies and responds — the nine-state spine. It may override the router (asset-count-aware dispatch, below).	core/controller.py; core/schemas.py (ControllerState); architecture/03-request-lifecycle.md
Specialist	A module that performs exactly one task and returns exactly one SpecialistResult. "Every specialist returns exactly this. No exceptions." (core/schemas.py:326). Five are registered.	specialists/; core/schemas.py (SpecialistResult); architecture/05-specialists.md §1
Evidence	A single, typed, inspectable record supporting a result. It is the project's answer to "never pretend it knows more than the evidence supports". Every Evidence has a type, a source_specialist, and (if spatial) a coordinate_system.	core/schemas.py:216 (Evidence); architecture/06-evidence-and-confidence.md §2
Evidence engine	The module that turns many specialists' evidence into one ordered, deduplicated, capped collection, and that applies calibration to confidence. Pipeline: collect → dedup → sort → annotate → renumber → cap.	evidence/engine.py; architecture/06-evidence-and-confidence.md §5
Confidence breakdown	The structured confidence object: a raw value, an optional calibrated value, a method ("uncalibrated" or "temperature_scaling"), a components map, and a degraded flag with a reason.	core/schemas.py:259 (ConfidenceBreakdown); architecture/06-evidence-and-confidence.md §7.2
Calibrated vs uncalibrated	Calibrated = a fitted transform was applied (method: "temperature_scaling", effective: true). Uncalibrated = no transform, or a T of exactly 1.0 (method: "uncalibrated"). Reporting uncalibrated is honest, not broken.	evidence/engine.py; core/schemas.py (ConfidenceBreakdown.method)
ResultEnvelope	The top-level response object of POST /v1/analyze: a run_id, a SpecialistResult, and the ExecutionTrace, plus a schema_version. The route returns it verbatim, trace included.	core/schemas.py:421; core/controller.py:394; architecture/08-api-contract.md
ExecutionTrace	The per-run record of what happened: run_id, task, query, inputs, modalities, intent, validation, workflow, steps, selected_models, parameters, outputs, confidence, timings, fallbacks, errors, contradiction, config_hash, started_at, finished_at. It is client-facing.	core/schemas.py:296; architecture/10-observability-and-ops.md §3
Intent	The router's structured output: a task, a modality, three booleans (temporal, spatial_output, language_output), a confidence, and a source (learned	lexical_fallback
EvidenceType	The closed 11-member vocabulary of evidence kinds: image_crop, tile, bounding_box, mask, change_map, optical_view, sar_view, joint_feature_region, statistic, geolocation, availability_mask.	core/schemas.py:65; architecture/06-evidence-and-confidence.md §3
CoordinateSystem	The frame a coordinate is expressed in. Exactly three values: normalized_0_1, pixel, geo. "Never omit this. A bare box is meaningless without it." (finding C-5).	core/schemas.py:57; architecture/06-evidence-and-confidence.md §4.1
Region	A general spatial result: an optional box, an optional mask_ref, a label, a score, and a coordinate_system.	core/schemas.py:189
ChangeRegion	A change-specific region: a required box, area_pixels, mean_probability, an optional stability, and a coordinate_system.	core/schemas.py:202
Availability mask	A per-channel boolean vector recording which bands a sensor actually measured. It is consumed by the fusion head, not by CROMA (finding C-1) — because handing a masked autoencoder a mask invites it to reconstruct missing channels, precisely the fabrication the sensor adapter exists to prevent.	core/schemas.py (SensorDescriptor.availability_mask, EvidenceType.AVAILABILITY_MASK); specialists/optical_sar/fusion_head.py; MODELS.md §3.0.1

E.2 Behavioural concepts

Term	Definition	Defined / enforced
Degraded	A result flag meaning "the analysis could not be fully performed" — e.g. no trained detector, or co-registration too poor to support a spatial claim. It is not "no change was detected", which is a normal, successful outcome.	core/schemas.py:343 (SpecialistResult.degraded); core/schemas.py:353-406 (the validator, incl. the F-16c correction); architecture/05-specialists.md §4.2
Fallback	A recorded degradation, named in ExecutionTrace.fallbacks as a human-readable string. It records what degraded, not what was retried.	core/schemas.py:314; architecture/10-observability-and-ops.md §3.7
Lexical fallback	The deterministic, model-free router path: ordered lexical rules, highest specificity first, first match wins. It has no model, no embeddings, no randomness, and it "never invents capability" — if nothing matches it returns unsupported with low confidence. Precedence: dual_modality > temporal > spatial > caption > vqa > unsupported. Used when the learned router is low-confidence, or when the encoder cannot load at all.	router/fallback.py (lexical_route, self_check); core/schemas.py (Intent.source = "lexical_fallback")
Dispatch	The controller's act of choosing which specialist to run for a request. Dispatch is asset-count-aware: a change-style query with one asset reads change but dispatches change_vqa. The reading is asset-count-blind; the dispatch is not.	core/controller.py; architecture/04-router.md; LIMITATIONS.md item 13
Asset-count-aware	The property that a decision accounts for how many assets were supplied. Exactly 2 assets for change/change_vqa/optical_sar; exactly 1 for vqa/caption. "A silent 'just use the first two' produces a confident answer to a question the caller did not ask."	core/planner.CAPABILITY_ASSETS; SpecialistSpec.requires_assets; MODELS.md §3.0.3
Preview path	The frontend's no-file demonstration mode. It emits all eight production event names — the common summary "it emits no specialist events" is a correction: what the preview withholds is the content, not the event. Its specialist events carry a component name and no measurement; the result stages carry explicit emptiness.	frontend/assets/js/mission.js:550-578; architecture/09-frontend.md §5.2
Trace fill	The execution-trace progress bar's fill fraction. The measured live value is 94.4444 % (17/18) — the bar advances to the current node rather than to its right edge, so a fully-completed run stops at 94.4444 %, not 100 %.	architecture/06-evidence-and-confidence.md §13.3; README.md (trace-fill row); DOCS_STYLE_GUIDE.md §3
Run id	The identifier for one analysis run, generated as run_<12 hex>. It appears on the ResultEnvelope and inside the ExecutionTrace.	core/schemas.py:28 (_new_id), :299 (ExecutionTrace.run_id); architecture/10-observability-and-ops.md §5.1
Request id	A separate identifier namespace, req_<24 hex>, generated by the gateway and echoed as X-Request-Id. The two id spaces do not join — there is no trace-context propagation.	gateway/policy.py; architecture/10-observability-and-ops.md §5.2, §5.4
Config hash	A 16-hex-character stable digest of the whole config registry, computed in Config.hash over the JSON-sorted registry. It is recorded in every evaluation run and in every artifact. The frozen value is 78f1e3700da15aa1.	core/config.py:76-80; architecture/07-configuration-freeze.md
Frozen config	The config registry whose hash is 78f1e3700da15aa1 and which may not move without invalidating the benchmark. "Frozen" means validated against the frozen architecture at load time and hashed for reproducibility.	core/config.py; configs/base.yaml; REPRODUCIBILITY.md §2
Invariant	A property enforced at load time, not merely documented, because the failure it prevents is a silent shape error — a tensor of the right shape that trains to a worse number. Examples: fusion.input_dim == 2318, grounding_head.feature_dim == 2048, croma.image_resolution % 8 == 0.	core/config.py (_validate); MODELS.md §3.0; REPRODUCIBILITY.md §2.3

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Part F — Deployment and operations

Topology, transport and the runbook are in DEPLOYMENT.md, architecture/02-deployment-topology.md and architecture/10-observability-and-ops.md.

Term	Definition	Defined / enforced
Gateway	The Render-hosted edge service. It owns eight responsibilities — schema validation, size limits, rate limiting, CORS, request IDs, timeouts, secret custody, error translation — and makes no model decisions. It proxies four routes: /v1/health, /v1/capabilities, /v1/analyze, /v1/assets.	gateway/app.py, gateway/policy.py; docs/DEPLOYMENT_ARCHITECTURE.md §2.1
Orchestrator	The gateway's role as the component that starts the Codespace and waits for it, then proxies the request. Also called the hub.	docs/DEPLOYMENT_ARCHITECTURE.md; architecture/10-observability-and-ops.md §2.3
Tunnel	The live transport: an outbound long-poll (POST /tunnel/agent) from a Codespace agent to the hub. Direction is inverted — the Codespace dials out, which is why the design works with a private repository. A forwarded Codespace port returns HTTP 302 for a private repo, which is why the tunnel exists.	deploy/codespace/tunnel_agent.py; tunnel.py; architecture/02-deployment-topology.md §3
Long-poll	A request that the server parks until work arrives or a timeout fires, rather than returning immediately. The tunnel agent announces itself with a long-poll POST /tunnel/agent.	tunnel.py; architecture/02-deployment-topology.md §3
Wake flow	The fallback path: ensure_codespace_up() starts a stopped Codespace via the GitHub API and polls GET {base}/v1/health every 2 s (10 s per-poll timeout) until it answers 200, bounded by wake_timeout_s = 120.	apply-test/main.py:417-442; architecture/10-observability-and-ops.md §7.2
Cold start	The delay before a stopped Codespace (or a sleeping Render instance) can serve. Documented as "tens of seconds"; bounded worst case wake_timeout_s = 120 before a 504. No measured distribution exists.	DEPLOYMENT.md §8; architecture/10-observability-and-ops.md §7.2
Forward path	The fallback transport that proxies to a forwarded Codespace port directly. For a private repo it returns 302; in auto mode a tunnel timeout falls through to it, which is the root shape of B-07.	DEPLOYMENT.md §8.1; architecture/10-observability-and-ops.md §7.3
tunnel_offline	The error code (503) meaning no agent is connected and mode == "tunnel". The remedy is to start the Codespace.	apply-test/main.py:308-332; architecture/10-observability-and-ops.md §7.3
CORS allowlist	The explicit list of permitted browser origins, from SATQUERY_ALLOWED_ORIGINS. Never * — an empty or wildcard allowlist is refused at construction by GatewayConfig.__post_init__.	gateway/policy.py; docs/DEPLOYMENT_ARCHITECTURE.md §2.1
Rate limit	A per-IP and global request cap. Default rate_limit_per_ip = 10 over rate_limit_window_s = 60.0. It is a fairness control, not a protection control — it does not prevent a bypass, and it exists to protect the GPU budget.	gateway/policy.py; docs/DEPLOYMENT_ARCHITECTURE.md §5.2
LFS	Git Large File Storage — the mechanism for storing large binary files (model weights) in a Git repository by pointer. Used for the released artifacts on the Hub.	REPRODUCIBILITY.md §7; MODELS.md
Git Data API	The GitHub REST API used to create commits programmatically instead of git push. Chosen because it avoids a local clone/credential flow.	DEPLOYMENT.md §7.1

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Part G — Status vocabulary

The project uses a fixed status vocabulary on every substantive claim. "Never upgrade a status: NOT RUN never becomes PASS; OPEN never becomes VERIFIED; REJECTED never becomes ACCEPTED." (DOCS_STYLE_GUIDE.md §1.7).

Tag	Definition
IMPLEMENTED	The code exists. It does not imply it was run, or that it produced a good result.
VERIFIED	Measured and independently confirmed against a frozen criterion. In this project only the change headline carries it (pooled IoU 0.8122 / macro IoU 0.8457 / pooled F1 0.8964).
MEASURED	A number was produced by running something and recorded in an artifact. It does not imply acceptance.
ATTEMPTED	Work was done but did not complete cleanly or did not resolve the question (e.g. the BigEarthNet format contradiction).
NOT RUN	The work was not done. Never to be read as a pass.
BLOCKED	The work cannot be done in the environment or without an external resource. A fact, not a decision.
DEFERRED	The work could be done now but was deliberately postponed, with a reason.
REJECTED	The work was done and the answer was no. Used for the 448 grounding resolution and the ZeroGPU deployment target.
OPEN	The item is unresolved and not closed. Used for B-07, B-02, the missing LICENSE, and the 50,822-vs-51,725 comment discrepancy.
RESOLVED	A previously open item was closed with evidence.
CLOSED	The item is finished and no longer tracked.
USABLE_VERIFIED	The VLM adapter's state: its metrics are usable (exact_match 0.963), verified against the run record. It is not the same as accepted.
ACCEPTANCE-REJECTED	The artifact passed verification but was not promoted for production use. The VLM adapter is USABLE_VERIFIED and ACCEPTANCE-REJECTED — "Those are different words about different things." The deployed caption/VQA path uses the unadapted model.
TRAINED_UNVERIFIED	A training run produced an artifact that has not been evaluated on a held-out split. The change-VQA run record's state, with the note: "training produces an artifact, not a verified capability. R-02 reaches VERIFIED only after the returned checkpoint has been evaluated on the held-out split." Superseded by the promotion record but retained on disk.
OPEN ruling	A verdict that is not settled — the metric is measured but the acceptance decision is unmade. Used for optical-SAR (accuracy 0.931 with macro-F1 0.434161) and change-VQA (test 0.697626/0.378373; test2 0.651469/0.372309). "Never accuracy without macro-F1."

Related honesty rules that recur with these tags:

• USABLE ≠ ACCEPTED (EVALUATION.md Rule 7).
• Two protocols are never collapsed (EVALUATION.md Rule 2): grounding is reported under canonical (0.2838) and matched6 (0.2566).
• Two test sets are never collapsed (Rule 3): change-VQA has test and test2.
• Validation is not test (Rule 5): the router's 0.965116 is validation, ungated, n = 86; the test split was NOT RUN.
• A negative result stays negative (Rule 6): calibration made ECE worse and stays reported as worse.

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Part H — Index — where each term is defined or enforced

Term / concept	Primary file(s)
Task vocabulary (7 tasks)	core/schemas.py (Task); router/label_space.py (TASK_CLASSES)
Modality vocabulary	core/schemas.py (Modality); router/label_space.py (MODALITY_CLASSES)
Intent, EvidenceType, CoordinateSystem, Region, ChangeRegion	core/schemas.py
SpecialistResult, ResultEnvelope, AnalysisRequest, HealthStatus	core/schemas.py
ExecutionTrace, TraceStep, ModelRef, ConfidenceBreakdown, Evidence	core/schemas.py
Config hash, invariants, device resolution	core/config.py
Frozen values and budgets	configs/base.yaml (frozen configs/deploy.yaml is inert)
Router encoder / adapter / label space / fallback	router/encoder.py, router/adapter.py, router/label_space.py, router/fallback.py
Controller, dispatch, nine-state spine	core/controller.py
Planner, capability asset counts	core/planner.py
Evidence engine, calibration, confidence	evidence/engine.py, evidence/confidence.py
Specialists	specialists/vqa/, specialists/grounding/, specialists/change/, specialists/optical_sar/
Input-quality gate, percentile stretch	preprocessing/quality.py, preprocessing/imagery.py, preprocessing/raster.py
Gateway policy, rate limit, CORS, error taxonomy	gateway/policy.py, gateway/app.py, gateway/assets.py
Deployment report / capability adapter	app/deployment.py, app/space_app.py
Six artifact byte counts and digests	../models/manifest.json, ../models/checksums.sha256
Status vocabulary	DOCS_STYLE_GUIDE.md §2; EVALUATION.md §2

Cross-references

• PERFORMANCE.md — the footprint, the component timings, the cost traps, and the explicit statement that no end-to-end benchmark exists.
• MODELS.md — the artifact and backbone reference, with enforced invariants.
• BENCHMARKS.md — every measured metric with its artifact key path.
• EVALUATION.md — the eight honesty rules and the protocols.
• DATASETS.md — the corpora, splits and leakage controls.
• TRAINING.md, REPRODUCIBILITY.md — how the artifacts were made and how the claims are re-checked.
• DEPLOYMENT.md — the live topology, transport and cold start.
• LIMITATIONS.md — the exhaustive catalogue of what is not done or not known.
• architecture/05-specialists.md, architecture/06-evidence-and-confidence.md, architecture/10-observability-and-ops.md — the deep chapters for the specialists, the evidence/confidence subsystem, and operations.

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Terms deliberately not defined here

The following appear in some drafts but are not established by any file read, so this glossary does not define them: a general "quality score", a "system accuracy", an "end-to-end latency", a "cost-per-request", or a "throughput". Where a reader needs one of these, the honest answer is UNKNOWN — not established from the available evidence, and the place to look is PERFORMANCE.md §8 and LIMITATIONS.md §3.