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 594 species occurrences available in GBIF matching the query: { "and" : [ { "or" : [ "PublishingCountry is Canada", "Country is Canada" ] }, "TaxonKey is one of (Azolla Lam., Azolla caroliniana var. americana Nutt., 1818, Azolla caroliniana var. caroliniana, Azolla caroliniana Willd., Azolla coloniensis De Benedetti & Zamaloa, 2018, Azolla cristata Kaulf., Azolla filiculoides Lam., Azolla intertrappea Sahni & Rao, 1934, Azolla keuja Hermsen et al., 2019, Azolla mexicana C.Presl, Azolla nana Dorofeev, 1980, Azolla nilotica Decne., Azolla pinnata R.Br., Azolla pinnata subsp. africana (Desv.) R.M.K.Saunders & K.Fowler, Azolla ventricosa Dorofeev, 1955, Azolla vellus Jain & Hall, 1969, Azolla schopfii Dijkstra, 1961, Azolla rubra R.Br., Azolla prisca Reid & Chandler, 1926, Azolla primaeva Arnold, 1955, Azolla pinnata subsp. pinnata, Azolla pinnata subsp. asiatica R.M.K.Saunders & K.Fowler, Azolla mexicana Schlecht. & Cham., 1830, Azolla nilotica Decne. ex Mett., Azolla circinata Oltz & Hall, Azolla stanleyi Jain & Hall, 1969)" ] } The dataset includes 594 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011355-250402121839773/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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.022 |
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