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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
item_id: string
source_kind: string
source_name: string
source_record_id: string
text: string
text_sha256: string
split: string
group_id: string
source_url: string
author_name: string
author_url: string
license: string
web_record_count: int64
corpus_sha256: string
git_commit_sha: string
scientific_result: bool
web_records: string
surface_corpus_sha256: string
web_records_sha256: string
web_included_count: int64
artifact_version: string
surface_corpus: string
corpus_count: int64
environment: struct<cuda_visible_devices: null, slurm_job_id: null>
  child 0, cuda_visible_devices: null
  child 1, slurm_job_id: null
stage: string
web_excluded_count: int64
generated_at: string
reranking_outcomes_observed: bool
counts_by_source_and_split: struct<anthropic-hh-helpful-base|confirmation: int64, anthropic-hh-helpful-base|development: int64,  (... 343 chars omitted)
  child 0, anthropic-hh-helpful-base|confirmation: int64
  child 1, anthropic-hh-helpful-base|development: int64
  child 2, databricks-dolly-15k|confirmation: int64
  child 3, databricks-dolly-15k|development: int64
  child 4, stackexchange:askubuntu|development: int64
  child 5, stackexchange:diy|confirmation: int64
  child 6, stackexchange:stackoverflow|development: int64
  child 7, stackexchange:superuser|development: int64
  child 8, stackexchange:travel|confirmation: int64
  child 9, stackexchange:workplace|development: int64
semantic_labels_present: bool
to
{'artifact_version': Value('string'), 'corpus_count': Value('int64'), 'corpus_sha256': Value('string'), 'counts_by_source_and_split': {'anthropic-hh-helpful-base|confirmation': Value('int64'), 'anthropic-hh-helpful-base|development': Value('int64'), 'databricks-dolly-15k|confirmation': Value('int64'), 'databricks-dolly-15k|development': Value('int64'), 'stackexchange:askubuntu|development': Value('int64'), 'stackexchange:diy|confirmation': Value('int64'), 'stackexchange:stackoverflow|development': Value('int64'), 'stackexchange:superuser|development': Value('int64'), 'stackexchange:travel|confirmation': Value('int64'), 'stackexchange:workplace|development': Value('int64')}, 'environment': {'cuda_visible_devices': Value('null'), 'slurm_job_id': Value('null')}, 'generated_at': Value('string'), 'git_commit_sha': Value('string'), 'reranking_outcomes_observed': Value('bool'), 'scientific_result': Value('bool'), 'semantic_labels_present': Value('bool'), 'stage': Value('string'), 'surface_corpus': Value('string'), 'surface_corpus_sha256': Value('string'), 'web_excluded_count': Value('int64'), 'web_included_count': Value('int64'), 'web_record_count': Value('int64'), 'web_records': Value('string'), 'web_records_sha256': 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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
              item_id: string
              source_kind: string
              source_name: string
              source_record_id: string
              text: string
              text_sha256: string
              split: string
              group_id: string
              source_url: string
              author_name: string
              author_url: string
              license: string
              web_record_count: int64
              corpus_sha256: string
              git_commit_sha: string
              scientific_result: bool
              web_records: string
              surface_corpus_sha256: string
              web_records_sha256: string
              web_included_count: int64
              artifact_version: string
              surface_corpus: string
              corpus_count: int64
              environment: struct<cuda_visible_devices: null, slurm_job_id: null>
                child 0, cuda_visible_devices: null
                child 1, slurm_job_id: null
              stage: string
              web_excluded_count: int64
              generated_at: string
              reranking_outcomes_observed: bool
              counts_by_source_and_split: struct<anthropic-hh-helpful-base|confirmation: int64, anthropic-hh-helpful-base|development: int64,  (... 343 chars omitted)
                child 0, anthropic-hh-helpful-base|confirmation: int64
                child 1, anthropic-hh-helpful-base|development: int64
                child 2, databricks-dolly-15k|confirmation: int64
                child 3, databricks-dolly-15k|development: int64
                child 4, stackexchange:askubuntu|development: int64
                child 5, stackexchange:diy|confirmation: int64
                child 6, stackexchange:stackoverflow|development: int64
                child 7, stackexchange:superuser|development: int64
                child 8, stackexchange:travel|confirmation: int64
                child 9, stackexchange:workplace|development: int64
              semantic_labels_present: bool
              to
              {'artifact_version': Value('string'), 'corpus_count': Value('int64'), 'corpus_sha256': Value('string'), 'counts_by_source_and_split': {'anthropic-hh-helpful-base|confirmation': Value('int64'), 'anthropic-hh-helpful-base|development': Value('int64'), 'databricks-dolly-15k|confirmation': Value('int64'), 'databricks-dolly-15k|development': Value('int64'), 'stackexchange:askubuntu|development': Value('int64'), 'stackexchange:diy|confirmation': Value('int64'), 'stackexchange:stackoverflow|development': Value('int64'), 'stackexchange:superuser|development': Value('int64'), 'stackexchange:travel|confirmation': Value('int64'), 'stackexchange:workplace|development': Value('int64')}, 'environment': {'cuda_visible_devices': Value('null'), 'slurm_job_id': Value('null')}, 'generated_at': Value('string'), 'git_commit_sha': Value('string'), 'reranking_outcomes_observed': Value('bool'), 'scientific_result': Value('bool'), 'semantic_labels_present': Value('bool'), 'stage': Value('string'), 'surface_corpus': Value('string'), 'surface_corpus_sha256': Value('string'), 'web_excluded_count': Value('int64'), 'web_included_count': Value('int64'), 'web_record_count': Value('int64'), 'web_records': Value('string'), 'web_records_sha256': Value('string')}
              because column names don't match

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GEODML decision-readiness manifold pilot

This repository is the frozen public data snapshot for identifying an operational information-seeking to decision/action-readiness direction in a prespecified LLM2Vec representation. It precedes query-conditioned prompt generation.

The artifacts were produced by GEODML code commit bb88a23c48c8036b95159003c57171db099f2171. They are inputs to an experiment, not evidence that a one-dimensional semantic axis has already been established.

Contents

  • v1/corpus/semantic_readiness_corpus.jsonl: 5,091 licensed, exact-unique natural-text items from Dolly, Anthropic HH, and Stack Exchange.
  • v1/labeling/readiness_label_tasks_blinded.jsonl: 15,273 blinded tasks, comprising three independent judge slots for every corpus item.
  • v1/surface/surface_coverage_corpus.jsonl: 4,000 Dolly/HH items used to define development and locked surface-realization families.
  • CHECKSUMS.sha256: hashes for every uploaded source artifact.

Licensing and attribution

This is a mixed-license research artifact. Preserve each row's source, license, URL, and attribution fields when redistributing or deriving data. Dolly-derived records retain CC BY-SA 3.0 attribution, Anthropic HH-derived records retain the upstream MIT terms, and Stack Exchange records retain their per-record CC BY-SA version and source attribution.

The public snapshot intentionally excludes the private blinded-label codebook, raw web API responses, and web records whose content license could not be verified. No LLM judgments, embeddings, fitted axis, or scientific conclusion is included in v1.

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