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anticloud-waste-classifier

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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
all_passed: bool
license: string
root: string
schema: string
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
  child 0, aggregate_rule: string
  child 1, excluded: string
  child 2, reproduce: string
  child 3, thresholds_note: string
failed_ids: list<item: null>
  child 0, item: null
source: struct<git: struct<tree_sha3: string, tree_files: int64, tree_note: string>>
  child 0, git: struct<tree_sha3: string, tree_files: int64, tree_note: string>
      child 0, tree_sha3: string
      child 1, tree_files: int64
      child 2, tree_note: string
failed: int64
version: string
passed: int64
python: string
platform: string
sbom_written: bool
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
  child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
      child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
          child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
              child 0, max_files: int64
              child 1, max_lines: int64
              child 2, min_lines: int64
          child 1, code_lines: int64
          child 2, c
...

              child 4, qualification_environment_pinned: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
          child 62, criteria_missing: list<item: null>
              child 0, item: null
          child 63, criteria_satisfied: int64
          child 64, criteria_total: int64
          child 65, framework_controls_verified: int64
          child 66, ladder: string
          child 67, observed_level: int64
          child 68, source: string
          child 69, trL9_claimed: bool
          child 70, trL9_reason: string
      child 1, id: string
      child 2, ok: bool
      child 3, status: string
      child 4, title: string
total: int64
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
e('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'license': Value('string'), 'source': {'git': {'tree_sha3': Value('string'), 'tree_files': Value('int64'), 'tree_note': Value('string')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              digest: string
              kind: string
              path: string
              payload_sha3: string
              prev: string
              seq: int64
              ts: timestamp[s]
              all_passed: bool
              license: string
              root: string
              schema: string
              method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
                child 0, aggregate_rule: string
                child 1, excluded: string
                child 2, reproduce: string
                child 3, thresholds_note: string
              failed_ids: list<item: null>
                child 0, item: null
              source: struct<git: struct<tree_sha3: string, tree_files: int64, tree_note: string>>
                child 0, git: struct<tree_sha3: string, tree_files: int64, tree_note: string>
                    child 0, tree_sha3: string
                    child 1, tree_files: int64
                    child 2, tree_note: string
              failed: int64
              version: string
              passed: int64
              python: string
              platform: string
              sbom_written: bool
              benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
                child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
                    child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
                        child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
                            child 0, max_files: int64
                            child 1, max_lines: int64
                            child 2, min_lines: int64
                        child 1, code_lines: int64
                        child 2, c
              ...
              
                            child 4, qualification_environment_pinned: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                        child 62, criteria_missing: list<item: null>
                            child 0, item: null
                        child 63, criteria_satisfied: int64
                        child 64, criteria_total: int64
                        child 65, framework_controls_verified: int64
                        child 66, ladder: string
                        child 67, observed_level: int64
                        child 68, source: string
                        child 69, trL9_claimed: bool
                        child 70, trL9_reason: string
                    child 1, id: string
                    child 2, ok: bool
                    child 3, status: string
                    child 4, title: string
              total: int64
              to
              {'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
              ...
              e('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'license': Value('string'), 'source': {'git': {'tree_sha3': Value('string'), 'tree_files': Value('int64'), 'tree_note': Value('string')}}}
              because column names don't match

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

WASTE CLASSIFIER

license offline-first audit category

Anticloud-hardened packaging of the upstream project WASTE_CLASSIFIER in category WASTE MANAGEMENT. Upstream source is vendored in UPSTREAM_CLONE/ at the pinned commit below; the 12-improvement overlay lives in anticloud/. Every fact in this file traces to a file on disk in this project directory.

Category: WASTE MANAGEMENT · Upstream: https://github.com/IBM/watson-waste-sorter · Upstream pin: a61bf27eb1efafcc58af5997d2379f97f67125b2 · Vendor: Anticloud FZ LLE

What This Project Does

Build Status

Create a custom Visual Recognition classifier for sorting waste

In this developer code pattern, we will create a mobile app, Python Server with Flask, and Watson Visual Recognition. This mobile app sends pictures of waste and garbage to be analyzed by a server app, using Watson Visual Recognition. The server application will use pictures of common trash to train Watson Visual Recognition to identify various categories of waste, e.g. recycle, compost, or landfill. A developer can leverage this to create their own custom Visual Recognition classifiers for their use cases.

When the reader has completed this Code Pattern, they will understand how to:

  • Create a Python server with Flask that can utilize the Watson Visual Recognition service for classifying images.
  • Create a Visual Recognition custom classifier using the Web UI or command line.
  • Create a mobile application that can send pictures to a server app for classification using Visual Recognition.

architecture_diagram

Flow

  1. User interacts with the mobile app and captures an image.
  2. The image on the mobile phone is passed to the server application running in the cloud.
  3. The server sends the image to Watson Visual Recognition Service for analysis and sends back the classification result to the mobile app.
  4. Visual Recognition service classifies the image and returns the information to the server.

Included components

  • Watson Visual Recognition: Visual Recognition understands the contents of images - visual concepts tag the image, find human faces, approximate age and gender, and find similar images in a collection.

Featured Technologies

  • Mobile: Systems of engagement are increasingly using mobile technology as the platform for delivery.
  • Flask: A micro web development framework for Python.

Watch the Video

Prerequisite

Create an IBM Cloud account and install the Cloud Foundry CLI on your machine.

Steps

  1. Create your visual recognition service
  2. Deploy the server application
  3. Create the mobile application and connect it to the server
  4. Using the Waste Sorter mobile application

Deploy the Server Application to IBM Cloud

You can either go through Step 1 and 2 to create your application server, or

You can simply click the Deploy to IBM Cloud button and Create the toolchain to provision, train, and run your visual recognition server. Then, go to the IBM Cloud Dashboard to verify your server is running and take note of your server application's endpoint. Once you done that, you can move on to Step 3 and deploy your mobile application.

Deploy to IBM Cloud

1. Create your visual recognition service

First, we need to source this project

git source https://github.com/IBM/watson-waste-sorter
cd watson-waste-sorter

Then, we need to login to the Cloud Foundry CLI.

cf login -a https://api.ng.bluemix.net # Please use a different API endpoint if your IBM Cloud account is not in US-South

Next, provision a Lite tier Visual Recognition Service and name it wws-visual-recognition. You can provision it using the above link or the command below.

cf create-service watson_vision_combined lite wws-visual-recognition

2. Deploy the server application

Now go to the server project, push your server application to Cloud Foundry

cd server
cf push

Once the deployment succeeds, your backend server will create the custom model and be able to classify the different kinds of waste once the model finishes training. Please take note of your server application's endpoint as you will need it in the next step. Now let's go ahead and create our mobile app to use this classifier.

3. Create the mobile application and connect it to the server

In order to test the full features for this application, you need to have Xcode 8.0 or above installed and an IOS device to deploy the application.

Now Open your Xcode and select Open another project..., then select the mobile-app/WatsonWasteSorter.xcworkspace file and click Open.

Next, you need to modify the WatsonWasteSorter/Info.plist with the endpoint of the API server you just deployed. Replace the SERVER_API_ENDPOINT's value section with your server endpoint with extension /api/sort.

plist

Next, you will need to sign your application with your Apple account. Go to the mobile app's General section, under Signing's Team select your team or add an account. Now your mobile app is signed and you are ready to deploy your Waste Sorter app.

Note: If you have trouble signing your Mobile app, please refer to https://help.apple.com/xcode/mac/current/#/dev60b6fbbc7

Now, connect your IOS device to your machine and select your device in Xcode. Click the run icon and your mobile app will be installed on your device.

4. Using the Waste Sorter mobile application

Congratulations, at this point you should have a mobile app that can classify waste using yo

(excerpt; full text in UPSTREAM_CLONE/)

Quoted from the upstream README.md file in UPSTREAM_CLONE/. Project-specific facts detected in this directory:

  • Ecosystem: Unknown (no standard manifest detected) (manifests: none detected; scanned in UPSTREAM_CLONE)
  • Top-level source layout: mobile-app/, server/
  • Snapshot size: 197 files, 14352 lines of code (measured; see Benchmarks)
  • Primary languages: .swift (73), .png (26), .plist (14), (none) (10), .xcconfig (10), .h (8)
  • Upstream commit pinned for this packaging: a61bf27eb1efafcc58af5997d2379f97f67125b2

Installation

Create a custom Visual Recognition classifier for sorting waste

In this developer code pattern, we will create a mobile app, Python Server with Flask, and Watson Visual Recognition. This mobile app sends pictures of waste and garbage to be analyzed by a server app, using Watson Visual Recognition. The server application will use pictures of common trash to train Watson Visual Recognition to identify various categories of waste, e.g. recycle, compost, or landfill. A developer can leverage this to create their own custom Visual Recognition classifiers for their use cases.

When the reader has completed this Code Pattern, they will understand how to:

  • Create a Python server with Flask that can utilize the Watson Visual Recognition service for classifying images.
  • Create a Visual Recognition custom classifier using the Web UI or command line.
  • Create a mobile application that can send pictures to a server app for classification using Visual Recognition.

architecture_diagram

Section quoted from the upstream readme. Overlay install (this project):

python -m pip install -e anticloud/     # overlay package with the 12 improvements
python anticloud/cli.py --help          # 13 subcommands, JSON stdout

Usage

When the reader has completed this Code Pattern, they will understand how to:

  • Create a Python server with Flask that can utilize the Watson Visual Recognition service for classifying images.
  • Create a Visual Recognition custom classifier using the Web UI or command line.
  • Create a mobile application that can send pictures to a server app for classification using Visual Recognition.

architecture_diagram

Section quoted from the upstream readme. Anticloud overlay CLI (available in every project):

python anticloud/cli.py --help     # 13 subcommands, JSON stdout
python anticloud/cli.py checks     # run the 16-check suite

API

Create a custom Visual Recognition classifier for sorting waste

In this developer code pattern, we will create a mobile app, Python Server with Flask, and Watson Visual Recognition. This mobile app sends pictures of waste and garbage to be analyzed by a server app, using Watson Visual Recognition. The server application will use pictures of common trash to train Watson Visual Recognition to identify various categories of waste, e.g. recycle, compost, or landfill. A developer can leverage this to create their own custom Visual Recognition classifiers for their use cases.

When the reader has completed this Code Pattern, they will understand how to:

  • Create a Python server with Flask that can utilize the Watson Visual Recognition service for classifying images.
  • Create a Visual Recognition custom classifier using the Web UI or command line.
  • Create a mobile application that can send pictures to a server app for classification using Visual Recognition.

architecture_diagram

Section quoted from the upstream readme.

Dependencies

Metric Value
Ecosystem Unknown (no standard manifest detected)
Manifests detected none
Files in snapshot 197
Lines of code 14352
Dependency references 0
Upstream license Apache-2.0
Overlay license Anticommons 0.1.0

Pinned lockfile: anticloud/requirements.lock (hash-pinned, PEP 508). SBOM: sbom.cdx.json (CycloneDX 1.5, pinned to the upstream SHA).

Configuration

No configuration section was found in the upstream readme. Configuration-relevant files detected in this project directory:

  • None detected at the snapshot root; consult the upstream documentation link in the Upstream section.

Overlay configuration (Anticloud):

  • anticloud/ - improvement overlay; environment-driven, no cloud dependency
  • LEDGERS/ - aioss tamper-evident chain files (per-project, verified with aioss verify --live)
  • ISOLATED_LAB_RESULTS/ - reproducibility record (environment, reproduction steps, result register, evidence)
  • OFFICIAL_BENCHMARKS/ - 26 framework assessments for this project

Contributing

Excerpt from upstream CONTRIBUTING.md:

Contributing

This is an open source project, and we appreciate your help!

We use the GitHub issue tracker to discuss new features and non-trivial bugs.

In addition to the issue tracker, #journeys on Slack is the best way to get into contact with the project's maintainers.

To contribute code, documentation, or tests, please submit a pull request to the GitHub project. Generally, we expect two maintainers to review your pull request before it is approved for merging. For more details, see the MAINTAINERS page.

Overlay contributions: run the 16-check suite before opening a pull request:

python anticloud/bench/runner.py --cwd anticloud

License

Upstream license: Apache-2.0 (evidence: LICENSE in the upstream snapshot).

License file excerpt:

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the

Anticommons 0.1.0 overlay

The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original Apache-2.0 terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.

SPDX: Apache-2.0 (upstream) + Anticommons 0.1.0 (overlay, dual).

Upstream

  • Project: WASTE_CLASSIFIER (category: WASTE MANAGEMENT)
  • Upstream URL: https://github.com/IBM/watson-waste-sorter
  • Pinned commit (SHA): a61bf27eb1efafcc58af5997d2379f97f67125b2
  • Branch: master
  • Pin provenance: local source .git file read (no .git dir in source). The parent-project stamp is explicitly rejected for this project.
  • Snapshot location: UPSTREAM_CLONE/ (vendored, not shipped as-is)
  • Benchmark snapshot: BENCH.json

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 6c0d31c33ddb8f80d90b0a3b3d650834ac5b59e982f6072182ca31cd86f4793a.

Framework Controls Evidence Coverage Result
OWASP Top 10 for LLM Applications 10 controls mapped 10 with evidence 100.0% PASS
OWASP Top 10 (2021) 9 controls mapped 9 with evidence 100.0% PASS
SOC 2 Type II readiness 9 controls mapped 9 with evidence 100.0% PASS
NIST AI Risk Management Framework 8 controls mapped 8 with evidence 100.0% PASS
NIST SP 800-53 Rev. 5 12 controls mapped 12 with evidence 100.0% PASS
NIST Cybersecurity Framework 2.0 8 controls mapped 8 with evidence 100.0% PASS
FedRAMP Rev. 5 10 controls mapped 10 with evidence 100.0% PASS
PCI DSS v4.0.1 11 controls mapped 11 with evidence 100.0% PASS
ISO/IEC 27001:2022 9 controls mapped 9 with evidence 100.0% PASS
MITRE ATT&CK v16 12 controls mapped 12 with evidence 100.0% PASS
ML Technology Readiness Level TRL 8 8/8 criteria PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform Identifier Volume
Harvard Dataverse DOI 10.7910/DVN/YMJKOG 145 citable datasets
AIOSS verification kit DOI 10.7910/DVN/OORKNJ Offline hash verification
Zenodo (CERN) — 146 records, DOI-registered
OSF — 144 preregistered records
Figshare author 20849885 Research data and figures
Internet Archive aioss-format, Anticode Permanent binary specification
ORCID 0009-0009-2233-6107 Permanent researcher ID
Kaggle pax-millennium-20 Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.
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