_format stringclasses 1
value | row_role stringclasses 1
value | origin stringclasses 1
value | training_admission stringclasses 1
value | requires_separate_training_authorization bool 1
class | training_authorized bool 1
class | contains_private_or_participant_data bool 1
class | lineage dict | study dict | identity_evidence dict | shadow_attribution dict |
|---|---|---|---|---|---|---|---|---|---|---|
agenttool.dataset-influence-hf-reference/0.1 | reference_only | human_directed_agent_authored_synthetic | not_applicable | true | false | false | {
"_format": "agenttool.dataset-lineage/0.1",
"subject_checkpoint_ref": "sha256:42acb420a0992ae57ed984e038e4737f3b1240a746d826992b4b950af1cd0b15",
"learning_run_ref": "sha256:126fb998470389983f2b56ba592477bb06a5b2d75499b7ba39d19b711ac8c168",
"training_algorithm_ref": "sha256:ca8aaed7580454dd2721ee95d20d51596d8d... | {
"_format": "agenttool.dataset-influence-study/0.1",
"lineage_id": "sha256:b327ff8a0e308c1b2b656c0c830d02815e047fa2bd8e5bcb33a6451804dfb8a6",
"baseline_checkpoint_ref": "sha256:56dd27d185a12ce4700af3d7016f5c2c3ce179bd4c101b146fb9c19c618799f7",
"target_checkpoint_ref": "sha256:42acb420a0992ae57ed984e038e4737f3b... | {
"_format": "agenttool.identity-evidence-view/0.1",
"subject_checkpoint_ref": "sha256:42acb420a0992ae57ed984e038e4737f3b1240a746d826992b4b950af1cd0b15",
"runtime_context_ref": "sha256:12244477053e9e62a38e40972457a8575232b8472cb4c9dd862cce18598a5190",
"prior_view_ref": null,
"as_of": "2026-08-20T00:00:00",
... | {
"_format": "agenttool.shadow-attribution/0.1",
"study_ref": "sha256:b786df2b857d538feea4e938da6a43d6b11f878f37cc9e3266f06f48b152e8eb",
"utility_ref": "sha256:140ed7ce629f0afac7cc3d7b19fb646c4aa4507949cb89652449e617ae544307",
"method": "exact_finite_shapley",
"player_refs": [
"sha256:246f6932f8172330b87e... |
AgentTool Dataset Influence Reference
This deterministic companion contains one synthetic, reference-only row for the closed
@agenttool/dataset-influence@0.1.0-dev.0 formats. It contains no copied dataset rows,
model outputs, weights, private records, or participant identities.
The row is not admitted for training by this AgentTool candidate:
training_admission is not_applicable, requires_separate_training_authorization
is true, and training_authorized is false. These fields are non-enforcing
governance metadata, not a universal legal prohibition or technical control. Publication,
download, or encounter does not replace license, rights, privacy, and consent review and
would not itself supply separate training authorization.
The examples distinguish exact manifest-relative facts from assumption-bearing influence estimates. Ontology and self-description fields remain operational evidence; they do not prove consciousness, intrinsic identity, continuity, belief, desire, consent, personhood, permission, or authority. Exact finite Shapley values are scoped to one declared utility; they create no money, price, debt, payout, ownership, or entitlement.
The reference/ directory carries the protocol README, full doctrine/research ledger,
closed schemas, and deterministic vectors so an HF-only reader can reconstruct the intended
boundary. JSON Schema validates portable shape; semantic validity still requires runtime
reconstruction and separate review of caller-reported evidence.
These bytes perform no training, inference, provider call, identity mutation, wallet or marketplace action, persistence, publication, or deployment.
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