FUN Azores: A trait database for the marine species of the ridges, seamounts, and hydrothermal vents of the Azores, NE Atlantic
Bibliographic record
Abstract
Trait-based approaches that complement taxonomic-based studies have increased in popularity among the scientific community over the last decades. The collection of biological and ecological characteristics of species (i.e., traits) provides insight into species and ecosystem vulnerability to environmental and anthropogenic changes, as well as ecosystem functioning. While most of the available trait databases to date contain essential information to understand the functional diversity of a taxonomic group or functional group based on size, the FUN Azores trait database has an ecosystem-based approach that provides a comprehensive assessment of diverse fauna (meio-, macro-, and megafauna) from benthic and pelagic environments in the Azores Marine Park; including ridges, seamounts, and hydrothermal vents. We used a collaborative approach involving 30 researchers with different expertise to develop the trait database; which contains compiled data on 14 traits representing morphological, behavioral, and life history characteristics for 1210 species, across 10 phyla.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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 source (direct Gemma or distilled Codex), 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".