1.0.0-rc2-pre: index annotations and card

README.md

@@ -88,7 +88,7 @@ configs:
 
 # The Well for The Cell
 
-Version **1.0.0-rc1**, built 2026-10-21T23:05:48+00:00 from wcEcoli commit `46667c5237081d72ed1195ec9f7332cd21b3be5d`.
+Version **1.0.0-rc2-pre**, built 2026-10-30T22:50:30+00:00 by the well4cell builder at wcEcoli commit `db3415a72f007937b7ba463e05472b8e2a6255f4`. That is the code that built this repo, not a simulator commit; the simulations ran under the commits in `index.git_hash` (see Provenance), and `manifest.json` lists both (`builder_commit`, `simulator_commits`).
 
 Time series from the [wcEcoli](https://github.com/CovertLab/wcEcoli) whole-cell model of *Escherichia coli*. Each sample is **one cell generation**: the cell from birth to division (or to the step at which the simulation stopped), recorded every **1 s** of simulated time. Lineages are simulated for several generations, and the samples of one lineage are linked by `parent_id`. The 117,174 trajectories come from 31,101 lineages of 3,896 distinct cells (design x condition), grouped into five families by campaign tier.
 
@@ -101,17 +101,46 @@ Time series from the [wcEcoli](https://github.com/CovertLab/wcEcoli) whole-cell
 | `double_knockout` | T5 | two-gene knockouts | 375 | 3,000 | 11,951 |
 | **total** | | | 3,896 | 31,101 | 117,174 |
 
+## Limitations
+
+Read this before using the data. Every number below is computed from this release's index unless it says it was measured on 1.0.0-rc1; each limitation has an index column, so nothing needs a side table. Rows are flagged, never removed: filter with the columns named.
+
+1. **Partial knockouts.** A knockout zeroes whole transcription units (TUs), and the campaign mapped each gene to one TU. A gene that sits in several TUs kept transcribing from the others: 1,145 knockout cells, 35,552 trajectories (770 single, 180 media-shift and 195 double-knockout cells), confirmed on protein counts (lpxD, ispH and pgk stay at wild-type level in all four generations). Columns: `partial_knockout`, `knockout_target_tus_missing`; these rows are `superseded` and are re-run over every TU of the target, and `superseded_by` will name the replacement.
+2. **Collateral genes.** Of the 1,982 single knockouts that did silence their target, 913 also fully silence at least one other gene and 1,069 take at least one TU from another gene (up to 11 genes fully silenced: `T2/wzyE_KO/acetate` also silences rffC, rffG, rffH, rffM, wecA, wecB, wecC, wecE, wecF, wzxE, wzzE). A cell labelled `X_KO` is a knockout of X's TU, an operon knockout. Columns: `collateral_genes_full`, `collateral_genes_partial`.
+3. **Duplicate designs.** Genes that share a TU give the same simulation: 369 single-knockout groups (918 cells) and 78 `dynamic_media` groups (204 cells) have the same zeroed TUs and the same condition label, and same-seed copies are bit-identical (447 pairs, 13,108 trajectories compared byte for byte on rc1; the rest are flagged by design identity). With the glucose doses folded (4) the 1,763 single-knockout designs cover 2,752 cells. Columns: `design_id`, and `canonical` on one cell per design (81,393 trajectories). 20 multi-cell designs mix parameter sets or simulator commits: those cells are the same design, not bit-identical twins.
+4. **Splits.** 1.0.0-rc1's `split_by_lineage` was stratified by cell, so bit-identical twins sat on both sides (977 to 3,135 twin lineage pairs, depending on how the pairs are counted), and `split_by_gene` held out gene symbols rather than TUs (160 of 2,578 groups leaked, 18,502 trajectories). Both splits are now on designs: `split_by_lineage` assigns a design's seed as one unit and `split_by_tu` holds out TUs, so no twin crosses a split. `split_by_gene` is gone; its values remain at Hub revision `1.0.0-rc1`.
+5. **Glucose doses.** `basal`, `glc_20mM`, `glc_5mM` and `glc_2mM` are the same cell: glucose uptake is a switch, not a saturable rate, so the dose has nothing to act on (knockout in `glc_20mM` equals `basal` in 16,498 of 16,498 trajectories compared on rc1). `condition_effective` folds them into `basal` (19,364 rows). It also replaces the label of regulation cells, whose campaign condition the simulator ignores (e.g. `T4/bglJ/active` and `T4/cytR/active`, labelled `acetate` and `succinate`, ran in `minimal`): 19,715 rows in all have `condition_effective` different from `condition`. The 384 sinusoidal-media trajectories have none (a continuous mixture; read `media_a`/`media_b`); the glucose sinusoid is probably static basal for the same reason, but that was not checked.
+6. **Shifts, not adapted cells.** No wild-type or knockout run set an initial condition, so every one started from the cell fitted to `basal` (`medium_fitted_for` `minimal`) and a static medium is a switch at t = 0 (`shift_at_t0`, 61,660 trajectories). Every such lineage is a shift experiment, not an adapted cell (`unfitted_shift` `t0`, 61,660 trajectories in 1,992 cells): expression stays fitted to `basal` while replication initiation follows the new medium. In a carbon source generation 0 over-initiates and generations 1 and 2 divide early (acetate birth dry mass 354, 202, 146 fg in generations 0 to 2 on rc1); in `with_aa` (22,618 trajectories) generation 0 divides at the basal time (about 2,800 s) where a cell fitted to `with_aa` takes about 1,400 s (measured on rc1). The adapted range arrives only after this campaign's four generations. Cells fitted to their medium (`ppgpp_conc`, `tf_activity`, the amino-acid shift controls) are not shifts. A later switch into a medium the cell is not fitted for (`unfitted_shift` `mid_run`, 11,457 trajectories in 366 cells; placeholder media are `withdrawn` instead, 7) does the same after the switch: the 153 trajectories that divide one second after birth are all of this kind (`dynamic_media`, `0 minimal, 1200 minimal_acetate`, generation 2), not static acetate cells.
+7. **Placeholder media.** `minimal_minus_phosphate` and `minimal_no_glucose` were added to the campaign as placeholders with invented doubling times; the model has no starvation response (on rc1, protein synthesis continued unchanged until ATP ran out). 418 trajectories are born into `minimal_minus_phosphate` (0 divide) and 1,547 into `minimal_no_glucose` (898 divide). `withdrawn` marks every generation in which one is in effect: 3,565 trajectories in 200 cells, with `withdrawn_reason`.
+8. **Divided is not viable.** Division follows replication, not translation, so a cell can divide with its ribosomes stopped: 6,273 trajectories have `translation_collapse`, 4,188 of them divided, and in some lineages protein at birth halves every generation. 160 trajectories have 10 steps or fewer (`short_trajectory`), 153 of them divided (the one-second divisions of 6). `viable` = divided, not `translation_collapse` and not `short_trajectory`: 108,205 viable, 8,969 not.
+9. **No tRNA charging channel.** `aa_pool_size`, `ntp_pool_size`, `trna_charged_fraction` were in the exporter's channel list but were dropped between the simulation and the export, and the raw output they could be recomputed from was pruned, so no tRNA or amino-acid pool question can be answered from these rows.
+10. **Knockout versus wild type crosses parameter sets and code.** Single knockouts: 83,937 on `8ff93a58` / `c47683e1`. Wild type: 604 on `b1750cf0` / `c41efd75`, 29 on `8ff93a58` / `ddc88250`, 4 on `b1750cf0` / `e319d09f`, 4 on `b1750cf0` / `18a908d2`. Double knockouts: 11,776 on `b1750cf0` / `c41efd75`, 103 on `8ff93a58` / `c462ae99`, 58 on `8ff93a58` / `c47683e1`, 14 on `8ff93a58` / `ddc88250`. So the release's most obvious comparison, knockout against wild type, also compares two parameter sets (they differ by 11 keys added for the `cytR` fix, no changed values) and two code versions (the knockout commit changed amino-acid synthesis, FBA retries, equilibrium and lineage termination). Compare only rows with equal `kb_sha256` and `git_hash`; wild-type controls on the knockout commit and both parameter sets are being added.
+
+**What that leaves.** The knockout rows (111,829 trajectories, 3,727 cells), each counted at its first step, in the order of the 1.0.0-rc1 review:
+
+| step | trajectories | cells | review (rc1) |
+|---|---:|---:|---:|
+| the knockout did not happen (`superseded`): re-run | 35,552 | 1,145 | 35,552 / 1,145 |
+| a twin of another cell with the same label (operon mates; not `canonical`) | 12,562 | 431 | 12,562 / 431 |
+| a glucose-dose copy of a `basal` design (not `canonical`) | 10,723 | 360 | 10,723 / 360 |
+| t = 0 switch into a medium the cell is not fitted for (`unfitted_shift` `t0`; the review: carbon sources only): a shift experiment | 34,889 | 1,141 | 22,007 / 722 |
+| mid-run switch into a medium the cell is not fitted for (`unfitted_shift` `mid_run`; the review: acetate or no oxygen only): a shift experiment | 5,346 | 174 | 3,539 / 116 |
+| placeholder medium (`withdrawn`) | 1,998 | 112 | 2,030 / 116 |
+| usable: target silenced, no duplicate, no switch into a medium the cell is not fitted for, no placeholder medium | 10,759 | 364 | 25,416 / 837 |
+
+Partition: usable 10,759, relabel as shift experiments 40,235, redundant copies 23,285, re-run 35,552, withdraw 1,998. The flags differ from the review in two ways. Every switch into a medium the cell is not fitted for is a shift, not only carbon sources and anoxia: static `with_aa` (12,882 trajectories) and mid-run switches into `with_aa` move from usable to relabel, so every usable row is `basal`. And `withdrawn` is per generation: a lineage's first generations that divided before the switch into a placeholder are not withdrawn (cells are counted in every step one of their rows reaches). Even usable rows need the collateral genes read (158 cells, 4,665 trajectories touch another gene) and the viability filter (9,879 of 10,759 are `viable`).
+
 ## What is in this repo
 
 Which families of each config are uploaded here (size on the Hub):
 
-| family | trajectories | `summary` | `core` | `expression` | `metabolism` | `expression_1s` | `metabolism_1s` |
-|---|---:|---|---|---|---|---|---|
-| `wildtype_media` | 641 | yes (242.6 KB) | yes (103.0 MB) | yes (512.4 MB) | yes (502.3 MB) | yes (2.0 GB) | yes (4.9 GB) |
-| `single_knockout` | 83,937 | yes (23.9 MB) | yes (13.5 GB) | yes (59.9 GB) | yes (65.6 GB) | yes (237.4 GB) | yes (639.3 GB) |
-| `dynamic_media` | 18,599 | yes (6.2 MB) | yes (2.8 GB) | yes (11.7 GB) | yes (13.5 GB) | yes (46.4 GB) | yes (131.6 GB) |
-| `regulation` | 2,046 | yes (736.0 KB) | yes (323.9 MB) | yes (1.7 GB) | yes (1.6 GB) | yes (6.5 GB) | yes (15.6 GB) |
-| `double_knockout` | 11,951 | yes (4.0 MB) | yes (1.9 GB) | yes (8.8 GB) | yes (9.1 GB) | yes (35.0 GB) | yes (88.9 GB) |
+| family | trajectories | `summary` | `core` | `expression` | `metabolism` | `expression_1s` | `metabolism_1s` | `charging` |
+|---|---:|---|---|---|---|---|---|---|
+| `wildtype_media` | 641 | yes (242.6 KB) | yes (103.0 MB) | yes (512.4 MB) | yes (502.3 MB) | yes (2.0 GB) | yes (4.9 GB) | no rows |
+| `single_knockout` | 83,937 | yes (23.9 MB) | yes (13.5 GB) | yes (59.9 GB) | yes (65.6 GB) | yes (237.4 GB) | yes (639.3 GB) | no rows |
+| `dynamic_media` | 18,599 | yes (6.2 MB) | yes (2.8 GB) | yes (11.7 GB) | yes (13.5 GB) | yes (46.4 GB) | yes (131.6 GB) | no rows |
+| `regulation` | 2,046 | yes (736.0 KB) | yes (323.9 MB) | yes (1.7 GB) | yes (1.6 GB) | yes (6.5 GB) | yes (15.6 GB) | no rows |
+| `double_knockout` | 11,951 | yes (4.0 MB) | yes (1.9 GB) | yes (8.8 GB) | yes (9.1 GB) | yes (35.0 GB) | yes (88.9 GB) | no rows |
 
 Every config is complete: all five families are in this repo.
 
@@ -119,7 +148,7 @@ Every config is complete: all five families are in this repo.
 
 ## Configs
 
-Each config is one data product. `summary` is one row per trajectory and joins to `index.parquet` on `sample_id`: `n_steps` and, for each of the 18 scalar channels in the order of the product schema, `<channel>_mean`, `_min`, `_max`, `_first` and `_last` over the trajectory's 1 s `core` rows (int32 for `mrna_counts` and `n_oric` except `_mean`, float32 otherwise; the `growth_rate` statistics skip its null row 0, so `growth_rate_first` is the value at t = 1). For every family it also has 13 pathway proteome fractions, `pathway_translation_ribosome`, `pathway_transcription_rnap`, `pathway_stringent_response`, `pathway_glycolysis`, `pathway_tca_cycle`, `pathway_pentose_phosphate`, `pathway_respiration_atp`, `pathway_aa_biosynthesis`, `pathway_amr_efflux`, `pathway_oxidative_stress`, `pathway_global_regulators`, `pathway_proteostasis` and `pathway_cell_division` (float32, unit `fraction`): for each pathway of the Cellarium pathway panel, the pathway's protein counts over all protein counts at each 10 s `expression` row, averaged over the trajectory (Cellarium's definition; the gene lists are in Cellarium's `pathway_panel.py`). Every other config carries `sample_id`, `t`, `t_abs` and the 18 scalar channels; the matrix configs add per-gene or per-reaction columns. `expression` and `metabolism` keep the rows with `t % 10 == 0` plus each trajectory's last row (point samples, not averages); `expression_1s` and `metabolism_1s` keep every row. The split of every config is the family.
+Each config is one data product. `summary` is one row per trajectory and joins to `index.parquet` on `sample_id`: `n_steps` and, for each of the 18 scalar channels in the order of the product schema, `<channel>_mean`, `_min`, `_max`, `_first` and `_last` over the trajectory's 1 s `core` rows (int32 for `mrna_counts` and `n_oric` except `_mean`, float32 otherwise; the `growth_rate` statistics skip its null row 0, so `growth_rate_first` is the value at t = 1). For every family it also has 13 pathway proteome fractions, `pathway_translation_ribosome`, `pathway_transcription_rnap`, `pathway_stringent_response`, `pathway_glycolysis`, `pathway_tca_cycle`, `pathway_pentose_phosphate`, `pathway_respiration_atp`, `pathway_aa_biosynthesis`, `pathway_amr_efflux`, `pathway_oxidative_stress`, `pathway_global_regulators`, `pathway_proteostasis` and `pathway_cell_division` (float32, unit `fraction`): for each pathway of the Cellarium pathway panel, the pathway's protein counts over all protein counts at each 10 s `expression` row, averaged over the trajectory (Cellarium's definition; the gene lists are in Cellarium's `pathway_panel.py`). Every other config but `charging` (see tRNA charging) carries `sample_id`, `t`, `t_abs` and the 18 scalar channels; the matrix configs add per-gene or per-reaction columns. `expression` and `metabolism` keep the rows with `t % 10 == 0` plus each trajectory's last row (point samples, not averages); `expression_1s` and `metabolism_1s` keep every row. The split of every config is the family. `expression` and `metabolism` are exact row subsets of `expression_1s` and `metabolism_1s`; they are kept for loader compatibility and will be reconsidered at 1.0.0.
 
 | config | rows every | matrices (active columns) | trajectories | rows | size |
 |---|---|---|---:|---:|---:|
@@ -129,9 +158,14 @@ Each config is one data product. `summary` is one row per trajectory and joins t
 | `metabolism` | 10 s | `reaction_flux_matrix` (3,974), `exchange_flux_matrix` (82) | 117,174 | 31,830,394 | 90.3 GB |
 | `expression_1s` | 1 s | `mrna_counts_matrix` (2,773), `protein_counts_matrix` (4,174) | 117,174 | 316,751,845 | 327.4 GB |
 | `metabolism_1s` | 1 s | `reaction_flux_matrix` (3,974), `exchange_flux_matrix` (82) | 117,174 | 316,751,845 | 880.3 GB |
+| `charging` | 1 s | `aa_pool_size` (21), `ntp_pool_size` (4), `trna_charged_fraction` (21) | not in this release | | |
 
 Matrix columns are pruned to the columns that are non-zero at least once anywhere in the campaign (active columns: mrna_counts 2,773 of 3,133, protein_counts 4,174 of 4,310, reaction_flux 3,974 of 9,612, exchange_flux 82 of 87). The full column lists, with an `active` mask and each active column's position in the product (`product_column`), are in `provenance/reference/<matrix>.parquet`.
 
+## tRNA charging
+
+The `charging` config has one row per 1 s step, like `core`, but no scalars: `sample_id`, `t`, `t_abs`, `aa_pool_size` (fixed_size_list<float32>[21], molecules), `ntp_pool_size` ([4], molecules) and `trna_charged_fraction` ([21], fraction of each amino acid's tRNAs that is charged). Join it to `core` on (`sample_id`, `t`). Column k of a list is the row with `column == k` of `provenance/reference/charging_<column>.parquet` (21 amino acids, ATP/CTP/GTP/UTP). It holds only trajectories with index `trna_channels` true: runs converted from rc2 on (0 trajectories); every rc1 row has none, and never will (their raw output is deleted). Coverage: none in this release. Row 0 (`t` = 0) of every list is NaN: the simulator's listener writes an all-zero first row before its first step (checked at export), like `growth_rate`, which is null there.
+
 ## index.parquet
 
 One row per trajectory, sorted by family (tier order T1..T5), cell_id, seed, generation.
@@ -174,13 +208,30 @@ One row per trajectory, sorted by family (tier order T1..T5), cell_id, seed, gen
 | `termination_class` | string | none, time_limit, negative_counts, fba_infeasible, equilibrium_unstable, other |
 | `translation_collapse` | bool | Cellarium's translation collapse, `ribosome_elongation_rate_mean < 1` (aa/s) in the `summary` config; null for a trajectory without a summary row (see Outcomes) |
 | `termination_reason` | string | the simulator's termination message |
+| `trna_channels` | bool | the run's raw output recorded `aa_pool_size`, `ntp_pool_size` and `trna_charged_fraction` (false for every run converted before rc2; see Provenance) |
 | `division_time_sec` | float | `n_steps - 1` when divided, else null |
 | `final_mass_fg` | float | cell mass at the last row, fg |
 | `growth_rate` | float | mean instantaneous growth rate over the generation, 1/s |
 | `lineage_terminated` | bool | the lineage ended in some generation |
 | `lineage_generations` | int8 | generations present for this lineage |
-| `split_by_lineage` | string | train/val/test, whole lineages, stratified by cell within family |
-| `split_by_gene` | string | train/val/test holding out whole genes; non-knockout rows are train |
+| `condition_effective` | string | the condition that actually ran: the campaign condition of the medium at t = 0, with `glc_20mM`/`glc_5mM`/`glc_2mM` folded into `basal`; null for sinusoidal media (see Limitations) |
+| `medium_fitted_for` | string | medium of the condition the first cell was initialised (fitted) from; `minimal` for every wild-type and knockout cell whatever its label |
+| `shift_at_t0` | bool | the medium at t = 0 is not `medium_fitted_for`: a cell fitted to one medium switched into another at t = 0 (null for sinusoidal media) |
+| `unfitted_shift` | string | `t0` (= `shift_at_t0`) or `mid_run` (a later switch into a medium other than `medium_fitted_for`, glucose doses as minimal, placeholders excepted) when the cell meets a medium its expression was not fitted for; a shift experiment, not an adapted cell |
+| `partial_knockout` | bool | a targeted gene kept a transcription unit: the knockout did not silence it |
+| `knockout_target_tus_missing` | list<element: string> | transcription units of the targeted genes that were not zeroed (null without a knockout) |
+| `collateral_genes_full` | list<element: string> | other genes every transcription unit of which was zeroed (silenced with the target) |
+| `collateral_genes_partial` | list<element: string> | other genes that lost some but not all of their transcription units |
+| `superseded` | bool | replaced by a re-run (every partial knockout); kept for the record |
+| `superseded_by` | string | cell_id of the re-run that replaces this cell (null until it is published) |
+| `withdrawn` | bool | a placeholder medium with no mechanism in the model is in effect in this generation |
+| `withdrawn_reason` | string | why the row is withdrawn |
+| `design_id` | string | hash of everything the simulator was given (zeroed TUs, fitted-for medium, schedule with glucose doses folded, sinusoid, TF state, ppGpp factor, amino-acid shift); twins share it |
+| `canonical` | bool | true on one cell per design (keep `canonical` rows to count each experiment once) |
+| `short_trajectory` | bool | `n_steps <= 10` |
+| `viable` | bool | divided, not `translation_collapse` and not `short_trajectory`; null when that depends on a null `translation_collapse` |
+| `split_by_lineage` | string | train/val/test over a design's seeds: every lineage of every cell of a design with one seed is in one split, so twins never cross |
+| `split_by_tu` | string | train/val/test holding out whole transcription units; a row takes the most held-out split of its `knockout_tu_ids`; non-knockout rows are train |
 | `job_id` | int32 | campaign job id |
 | `attempt` | int8 | campaign job attempt |
 | `started_at` | string | simulation start time |
@@ -306,7 +357,7 @@ A generation can divide while its ribosomes have effectively stopped, because ch
 2. **Viability classification**: predict whether a generation is *not* terminated by the time limit (`termination_class != 'time_limit'`), i.e. whether the perturbed cell still divides.
 3. **Forecasting**: given the trajectory up to time t, predict the channels at t + h, with horizons h stated in seconds (e.g. 10, 60, 600 s) so 1 s and 10 s products compare directly.
 
-Use `split_by_lineage` (train 93,698 / val 11,731 / test 11,745) to hold out whole lineages, or `split_by_gene` (train 91,561 / val 12,627 / test 12,986) to hold out whole genes; both are fixed columns of the index (seed 20261008).
+Use `split_by_lineage` (train 93,703 / val 11,732 / test 11,739) to hold out whole replicate lineages of each design, or `split_by_tu` (train 90,837 / val 12,039 / test 14,298) to hold out whole transcription units; both are fixed columns of the index (seed 20261008) and both keep bit-identical twins on one side. For viability use `viable`, not `divided`; to count each experiment once keep `canonical`; leave out `superseded` and `withdrawn` rows (see Limitations).
 
 ## Provenance
 
@@ -316,7 +367,7 @@ Use `split_by_lineage` (train 93,698 / val 11,731 / test 11,745) to hold out who
 - Every trajectory ran with operons on and the steady-state tRNA charging model (`trna_charging` true, no kinetic charging), from one of two fitted parameter sets identified by `kb_sha256` and `parca_cache`; do not pool rows across the two parameter sets without checking `kb_sha256`.
 - `provenance/scan_runs.parquet`: every run directory read (32,072 runs), with its generation count and reference-id hash.
 - `provenance/failed_jobs.parquet`: 971 campaign jobs that failed, with the error message and the generations they left behind.
-- `provenance/export_qc.parquet`: the QC verdict of every trajectory in every config (585,870 checks, 0 failed). Trajectories that fail QC are left out of that config.
+- `provenance/export_qc.parquet`: the export checks of every trajectory in every config (585,870 trajectory x config writes checked, 0 failed): rows landed in the part file, steps are contiguous, counts are valid. A trajectory that fails is left out of that config. These checks say the rows were **written** correctly, not that the cell was **healthy**: the cells' outcomes are in `termination_class`, `translation_collapse` and `viable` (`manifest.json`: `export_checks` versus `cell_health`).
 - `stats/<family>.parquet`: per product, channel and matrix column: mean, population std, min, max and row count.
 - `manifest.json`: row and byte totals per config, counts per family.
 
@@ -336,8 +387,10 @@ CC BY 4.0 for everything in this repo. The wcEcoli model is by the Covert lab (S
 ```bibtex
 @misc{well4cell,
   title  = {The Well for The Cell: whole-cell E. coli simulation trajectories},
-  author = {{{TODO}}},
+  author = {Yermakov, Alexey},
   year   = {2026},
   url    = {https://huggingface.co/datasets/cSynBioSys/well4cell}
 }
 ```
+
+TODO(authors): the author list above is incomplete; it is completed before 1.0.0.

index.parquet

@@ -1,3 +1,3 @@
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-oid sha256:1303239e108eee8f8e1f69a81e3bc31883d4310a4762b45e433695642a56b222
-size 6006036
+oid sha256:31034fe2ee3a6c9645d6cdc4c0e1f5f995dbddd9c6097c05dc60dff5e03c4e07
+size 6068154

manifest.json

@@ -1 +1 @@
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