Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A dataset containing 143 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Human Observation", "OccurrenceStatus is Present", "TaxonKey is Plantae", "WaterBody is one of (Lake Superior, Lake Superior (lake & creek tribs), Lake Superior shore, Lake superior, LAKE MICHIGAN-LAKE HURON, Lake Michigan, Lake Michigan (lake & creek tribs), Lake michigan, LAKE HURON ?, Lake Huron, Lake Huron (lake & creek tribs), Lake Huron, Duncan Bay, Lake Huron, MI, Lake Hurron, Lake huron, Lake Erie, Lake Erie (lake & creek tribs), Lake Erie, La Plaisance Bay, Lake Erie, Misery Bay, Lake Erie; Ohio, Lake erie, Lake Ontario, Lake Ontario (lake & creek tribs), Lake ontario, St Mary's, Niagara River, Niagara Falls, Niagara, Niagara River (upper), Niagara River near Rose-Hill, Niagara River, East Branch, Niagara river, HURON R., HURON RIVER-LAKE ERIE, Huron R., Huron R. (MI), Huron River, Huron River - Lake Erie, Huron River, St. Lawrence River, Huron river, Erie Lake, Erie/Huron, Ontario lake, Michigan Lake)" ] } The dataset includes 143 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016970-230224095556074/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.828 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it