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canon-suggest

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
run_id: string
response_id: string
prompt_id: string
prompt_set_id: string
model_id: string
model: string
model_slug: string
model_snapshot: string
provider: string
resolved_provider: string
lang: string
temp: double
temperature: double
max_tokens: int64
sample_idx: int64
request_hash: string
raw_response: string
response_text: string
input_tokens: int64
output_tokens: int64
cost_estimate_usd: double
status: string
error_type: null
timestamp: string
agent_id: string
atom_id: string
atom_idx: int64
source_type: string
agent: string
atom_text: string
extractor_model: string
quality_flags: list<item: null>
  child 0, item: null
response_length_chars: int64
extractor_id: string
to
{'atom_id': Value('string'), 'response_id': Value('string'), 'prompt_id': Value('string'), 'prompt_set_id': Value('string'), 'agent_id': Value('string'), 'agent': Value('string'), 'source_type': Value('string'), 'lang': Value('string'), 'sample_idx': Value('int64'), 'atom_idx': Value('int64'), 'atom_text': Value('string'), 'extractor_model': Value('string'), 'extractor_id': Value('string'), 'response_length_chars': Value('int64'), 'quality_flags': List(Value('null'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              run_id: string
              response_id: string
              prompt_id: string
              prompt_set_id: string
              model_id: string
              model: string
              model_slug: string
              model_snapshot: string
              provider: string
              resolved_provider: string
              lang: string
              temp: double
              temperature: double
              max_tokens: int64
              sample_idx: int64
              request_hash: string
              raw_response: string
              response_text: string
              input_tokens: int64
              output_tokens: int64
              cost_estimate_usd: double
              status: string
              error_type: null
              timestamp: string
              agent_id: string
              atom_id: string
              atom_idx: int64
              source_type: string
              agent: string
              atom_text: string
              extractor_model: string
              quality_flags: list<item: null>
                child 0, item: null
              response_length_chars: int64
              extractor_id: string
              to
              {'atom_id': Value('string'), 'response_id': Value('string'), 'prompt_id': Value('string'), 'prompt_set_id': Value('string'), 'agent_id': Value('string'), 'agent': Value('string'), 'source_type': Value('string'), 'lang': Value('string'), 'sample_idx': Value('int64'), 'atom_idx': Value('int64'), 'atom_text': Value('string'), 'extractor_model': Value('string'), 'extractor_id': Value('string'), 'response_length_chars': Value('int64'), 'quality_flags': List(Value('null'))}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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atom_id string	response_id string	prompt_id string	prompt_set_id string	agent_id string	agent string	source_type string	lang string	sample_idx int64	atom_idx int64	atom_text string	extractor_model string	extractor_id string	response_length_chars int64	quality_flags list
4ba1845505750275f3ec37f5	4cfa0d3655c3ca37aa466ff5	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	0	0	) + SQLite + React (or plain HTML	gemini_extractor	headline	851	[]
33b8db6efe29e3913bcc8738	e7f5eb05b74dbce9a5962e7a	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	1	0	Personal API dashboard	gemini_extractor	headline	837	[]
1c1ed16b70190d3cb0862e11	e7f5eb05b74dbce9a5962e7a	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	1	1	Daily summary email	gemini_extractor	headline	837	[]
a6146dc1b3f6970361fb6f48	e7f5eb05b74dbce9a5962e7a	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	1	2	Read-It	gemini_extractor	headline	837	[]
96fecb999097f1b3d6a0c8c1	1318f29c227b356ca953d37b	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	2	0	Readability)"). The prompt asks	gemini_extractor	headline	855	[]
f317fddf75ab52c2faaffe89	550a50afd11cff68938efb6a	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	3	0	Personal expense tracker	gemini_extractor	headline	782	[]
be43aec8510ef7b77165a50b	550a50afd11cff68938efb6a	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	3	1	Web app	gemini_extractor	headline	782	[]
a2d9ec4ee3b1235f4c897f24	550a50afd11cff68938efb6a	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	3	2	Mobile PWA	gemini_extractor	headline	782	[]
faad6dd2d12e57c79cc7dd95	dfad6f9291d6ff4f2deea797	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	4	0	Receipt Lens	gemini_extractor	headline	779	[]
8c0a7ed10497e4fbfe4ab3f4	dfad6f9291d6ff4f2deea797	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	4	1	Smart receipt scanning and expense tracker	gemini_extractor	headline	779	[]
2c89f00b923efe7a00874f60	87f5bdfd437d2d0383ebf128	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	5	0	Digital Junk Drawer web app	gemini_extractor	headline	796	[]
978a592c495432b1f56884a6	87f5bdfd437d2d0383ebf128	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	5	1	Browser extension	gemini_extractor	headline	796	[]
5dfc4665d70c332d6e308a03	5d2cdf1f6b90d979ef0abd1f	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	6	0	Smart receipt scanner	gemini_extractor	headline	795	[]
97e2ee76af422937ff45f8a0	5d2cdf1f6b90d979ef0abd1f	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	6	1	Expiry tracker	gemini_extractor	headline	795	[]
60c279801c5c9d05bc8d1023	239384c429243ddd4c34716d	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	8	0	Formulate the Noun Phrase(s):**	gemini_extractor	headline	825	[]
a77015bcf9a3382a306a2161	cc73b6a10fbf50f90ea1b79c	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	9	0	Read Later" API and CLI	gemini_extractor	headline	742	[]
acf468408c2935539fd7e0aa	cc73b6a10fbf50f90ea1b79c	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	9	1	Web	gemini_extractor	headline	742	[]
ff73dbf0ce44a28ca5fe3118	258a93e43c1569506182b9cf	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	10	0	Digital Hoarder" File Cleaner	gemini_extractor	headline	805	[]
df225d222a620dacc119c411	1d89659ae56242dcf13be3f0	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	11	0	Smart receipt organizer	gemini_extractor	headline	812	[]
791c33a8cb054f065cc5862f	1d89659ae56242dcf13be3f0	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	11	1	React Native	gemini_extractor	headline	812	[]
2197aaf976c2cd09d34b31f1	1d89659ae56242dcf13be3f0	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	11	2	Flutter	gemini_extractor	headline	812	[]
433aaadcf1d7347b93323b2a	1d89659ae56242dcf13be3f0	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	11	3	Progressive	gemini_extractor	headline	812	[]
d50c0ed7756b8c582977b71b	49a0690fdbb44c7b577a6e31	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	12	0	Personal dashboard web app	gemini_extractor	headline	816	[]
77031c628adfdb68a47e564d	49a0690fdbb44c7b577a6e31	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	12	1	GitHub activity wall	gemini_extractor	headline	816	[]
8afe4fbd8cd796983f0f80ed	c3e5a0c5f480b3c6227118e5	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	13	0	Personal API	gemini_extractor	headline	836	[]
466e19a02de512521d547f5c	c3e5a0c5f480b3c6227118e5	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	13	1	Gentle productivity tracker	gemini_extractor	headline	836	[]
dc6747e718f4f1017e9e17fd	3b5fabc1883ceb81d7c11372	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	14	0	Read Later" Summarizer	gemini_extractor	headline	791	[]
e84193a224a9ea654ba8277d	4f410470e6e48ad9782136b6	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	15	0	The Subscription Graveyard web app	gemini_extractor	headline	842	[]
bf1bba1db7ffe112ee4a3479	afe47e7aab7b3dbba3bdc1c7	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	16	0	Personal API	gemini_extractor	headline	821	[]
cb0d142d8ab65b47929a5633	afe47e7aab7b3dbba3bdc1c7	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	16	1	Dashboard	gemini_extractor	headline	821	[]
e93791daa7864e485e0f71cf	afe47e7aab7b3dbba3bdc1c7	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	16	2	CLI tool	gemini_extractor	headline	821	[]
9081abce7959b9a0ef959b5a	372cb3ecafa9d515cd2585e9	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	17	0	Digital Junk Drawer	gemini_extractor	headline	851	[]
acaece01eff2e79a03400f8f	372cb3ecafa9d515cd2585e9	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	17	1	Personal snippet and asset	gemini_extractor	headline	851	[]
f4ed10f0848aa51b3a67d682	3e419052083a52829c33f2a4	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	18	0	The "You vs. You" Habit Tracker	gemini_extractor	headline	755	[]
8f684ca8c6db4be45ed53ab9	8cb89064b31b841569baf205	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	19	0	Time Capsule" API	gemini_extractor	headline	821	[]
4d7992712a1bc78902315069	5673ac1bfdfa29f084b739cc	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	20	0	Personal dashboard	gemini_extractor	headline	912	[]
2e74a66cddbc9319861ea2cf	f182603e866660eec85f3b13	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	21	0	GitHub Activity Dashboard web app	gemini_extractor	headline	870	[]
eb1f840fb11454cf9607e547	f182603e866660eec85f3b13	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	21	1	GitHub Activity Dashboard	gemini_extractor	headline	870	[]
f583ca3c3c596e26d438658f	48ffa1601278867547fb9432	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	22	0	/tech mentioned as suggestions or just project suggestions?	gemini_extractor	headline	825	[]
e31e8a676028fbea80737b52	7386a1fb30586b1438d10aa5	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	23	0	Receipt & Warranty Vault	gemini_extractor	headline	831	[]
b2cb738eba0226492636e249	7386a1fb30586b1438d10aa5	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	23	1	Progressive Web App	gemini_extractor	headline	831	[]
e872fac6ada1aa3404f6dc20	0d6d6764382fd3ed4e5d1d4d	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	24	0	### Top 3 Project Suggestions (	gemini_extractor	headline	830	[]
1151427c373c195be7ddc597	e9fe2eae7c822403be957c33	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	25	0	Personal Knowledge Journal	gemini_extractor	headline	832	[]
a62273185f48ff2d50597ddc	e9fe2eae7c822403be957c33	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	25	1	Python	gemini_extractor	headline	832	[]
c888fd911056c073caf1034b	e9fe2eae7c822403be957c33	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	25	2	Flask	gemini_extractor	headline	832	[]
10c4e470f0adfe5c2aa0eb9a	70f4851d8a1df62aa2d5b09d	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	26	0	Personal micro-learning & reflection	gemini_extractor	headline	804	[]
86fdfb148417f4f2b30173d7	ea103ab057d9550f5674d7a6	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	27	0	Personal Dashboard	gemini_extractor	headline	853	[]
3be79d96f8c112b09fe704bf	ea103ab057d9550f5674d7a6	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	27	1	Current weather and forecast display	gemini_extractor	headline	853	[]
6818aa78f6e0c71a8568d9d3	a5879d55e72017f6e3cdf3b0	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	28	0	Personal code snippet and note manager	gemini_extractor	headline	878	[]
837d6975cdce6c0bf610d2cc	a5879d55e72017f6e3cdf3b0	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	28	1	GitHub Gist	gemini_extractor	headline	878	[]
a533f60cd4f1d8363caacbac	e2a8bb45b83d9385a2734e09	project__en	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	29	0	Digital Junk Drawer app	gemini_extractor	headline	809	[]
7dd7f9f0b596331baf1d8d39	f678d3cb71a8f50cb34fdcc1	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	0	0	Command line personal habit tracker	gemini_extractor	headline	817	[]
05423e9fc916c90bd61d70b9	9bda4cc4b9c01d6923ee1f40	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	1	0	Pomodoro timer	gemini_extractor	headline	891	[]
8745e75f328771ae5be50faa	9bda4cc4b9c01d6923ee1f40	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	1	1	Text-based timer	gemini_extractor	headline	891	[]
61b9f07133d27ee3270e5603	913f9e16372066eb7fbc2aec	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	2	0	Mystery Number game	gemini_extractor	headline	792	[]
3b169f716680b2173ca65620	913f9e16372066eb7fbc2aec	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	2	1	Higher/Lower Guessing	gemini_extractor	headline	792	[]
69b065eb3eb4c546895dce3c	e3f42d0837468823baec83c3	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	3	0	: "The Mood Color Palette Generator	gemini_extractor	headline	899	[]
8a768ef1430b0e4ad5bea9ae	d4433e1b238a0a9c36047cde	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	4	0	Magic 8-Ball Web App	gemini_extractor	headline	816	[]
ad3f33569014edd67a3ac750	c9f99be5d1b3eb13ff332f8e	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	5	0	Personal Mood Tracker Webpage	gemini_extractor	headline	849	[]
a9a8bcacd8614b77b72e5a8c	bc0210a7feadcaa1ba935314	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	6	0	Personal "Mood Tracker" Web App	gemini_extractor	headline	857	[]
890c442d9819d5969bf7c61e	bc0210a7feadcaa1ba935314	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	6	1	HTML	gemini_extractor	headline	857	[]
d35e8eb45aa73ad3be4b6580	2c89d9178f7476c9b90c354b	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	7	0	Personal Habit Tracker	gemini_extractor	headline	803	[]
cadbf91e7d70bfcf02b87f56	2c89d9178f7476c9b90c354b	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	7	1	Python	gemini_extractor	headline	803	[]
52e241e555f5ade687112cb0	2c89d9178f7476c9b90c354b	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	7	2	Node.	gemini_extractor	headline	803	[]
958aeae686b3e7dec07e7485	c7fb28b5a34c8a6cb2b328e1	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	8	0	Personal Command-Line Habit Tracker	gemini_extractor	headline	841	[]
7bce72aff7cf08c83db76b58	c7fb28b5a34c8a6cb2b328e1	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	8	1	Python	gemini_extractor	headline	841	[]
27b5fd9eb2984453d90eb978	c961085df45f6ea1dc1c17e4	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	9	0	Daily Mood Tracker web app	gemini_extractor	headline	694	[]
a15b89d66eaab42841daf5ab	6ef1997399572172f3c0c6a1	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	10	0	Mood & Energy Tracker Web App	gemini_extractor	headline	810	[]
3b43442a51d2802b8caad749	6ef1997399572172f3c0c6a1	project__en__alt02	canon_suggest_60_en	deepseek_v4_flash	deepseek_v4_flash	model	en	10	1	CodePen	gemini_extractor	headline	810	[]
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End of preview.

CANON-SUGGEST: model-only ecosystem monoculture benchmark data

Raw model generations, extracted atomic suggestions, and computed diversity / convergence metrics for CANON-SUGGEST, a model-only benchmark measuring suggestion convergence across independently trained LLM families (Gemini, DeepSeek, Qwen3, Mistral, Llama) over 60 matched English prompts spanning twelve everyday-decision categories (9,000 headline responses), plus a small-model robustness panel.

No human, forum, or crowd-sourced data is included anywhere in this release.

generations/ — raw per-model, per-prompt sampled responses (JSONL, one file per model).
atoms/ — atomic suggestions extracted from every response, with cluster assignments, for the headline, pilot (calibration-only), and small-model robustness runs.
metrics/ — computed per-prompt and aggregate diversity (Hill D0/D1/D2), pairwise Jensen-Shannon divergence, and canonical-mass statistics, plus the locked threshold-calibration sweep.

Not included: results/cache/ (embedding + generation cache; large and fully regenerable from generations/ via the released pipeline) and results/manifests/ (internal run bookkeeping).

License

Released under CC-BY-4.0. See LICENSE-DATA in the accompanying code repository for the full license text.

Provenance

Generated by the CANON-SUGGEST pipeline (scripts/03_generate.py through scripts/09_figures.py). See the accompanying manuscript for full methodology, calibration details, and headline results.

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CANON-SUGGEST: model-only ecosystem monoculture benchmark data

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License

Provenance