Bibliographic record
Abstract
A dataset containing 117499 species occurrences available in GBIF matching the query: { "and" : [ "TaxonKey is Ficus L.", { "or" : [ "Country is Latvia", "Country is Luxembourg", "Country is Lithuania", "Country is Libya", "Country is Ecuador", "Country is Lesotho", "Country is Algeria", "Country is Venezuela (Bolivarian Republic of)", "Country is Montenegro", "Country is Dominican Republic", "Country is North Macedonia", "Country is Germany", "Country is Uzbekistan", "Country is Uruguay", "Country is Moldova, Republic of", "Country is Morocco", "Country is Mexico", "Country is United States of America", "Country is Malawi", "Country is Myanmar", "Country is Uganda", "Country is Ukraine", "Country is Ethiopia", "Country is Spain", "Country is Eritrea", "Country is Netherlands", "Country is Estonia", "Country is Namibia", "Country is Tanzania, United Republic of", "Country is New Caledonia", "Country is Georgia", "Country is New Zealand", "Country is United Kingdom of Great Britain and Northern Ireland", "Country is Nepal", "Country is Norway", "Country is France", "Country is Falkland Islands (Malvinas)", "Country is Finland", "Country is Poland", "Country is Guatemala", "Country is South Georgia and the South Sandwich Islands", "Country is Greece", "Country is Pakistan", "Country is Peru", "Country is Papua New Guinea", "Country is South Africa", "Country is Croatia", "Country is Romania", "Country is Hungary", "Country is Indonesia", "Country is Zimbabwe", "Country is Ireland", "Country is Austria", "Country is Argentina", "Country is India", "Country is Australia", "Country is Iraq", "Country is Iran (Islamic Republic of)", "Country is Yemen", "Country is Iceland", "Country is Azerbaijan", "Country is Italy", "Country is Bosnia and Herzegovina", "Country is Portugal", "Country is Afghanistan", "Country is Albania", "Country is Angola", "Country is Paraguay", "Country is Armenia", "Country is Japan", "Country is French Southern Territories", "Country is Belarus", "Country is Jordan", "Country is Brazil", "Country is Tajikistan", "Country is Bhutan", "Country is Tunisia", "Country is Turkmenistan", "Country is Canada", "Country is Türkiye", "Country is Bulgaria", "Country is Syrian Arab Republic", "Country is Bangladesh", "Country is Belgium", "Country is Bolivia (Plurinational State of)", "Country is Kyrgyzstan", "Country is Kenya", "Country is Czechia", "Country is Cyprus", "Country is Korea (Democratic People’s Republic of)", "Country is Sweden", "Country is Korea, Republic of", "Country is Slovenia", "Country is Slovakia", "Country is Kazakhstan", "Country is Serbia", "Country is Congo", "Country is Switzerland", "Country is Lebanon", "Country is Russian Federation", "Country is Colombia", "Country is China", "Country is Saudi Arabia", "Country is Chile" ] } ] } The dataset includes 117499 records from 380 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004341-150316153013904/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.
How this classification was reachedexpand
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) | 0.000 | 0.146 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".