Functional diversity and biogeography using ‘sFDvent’ - the first global trait database for hydrothermal vent species
Bibliographic record
Abstract
The taxonomic composition of hydrothermal vent communities differs markedly on a global scale, forming distinct biogeographic provinces. The relative biodiversity of these areas can be assessed using traits as a common, cross-province ‘currency’. First, we used well-studied Juan de Fuca Ridge vents (NE Pacific) to assess trait data availability for vent species and to test the performance of functional diversity metrics given a species-poor system. These investigations highlighted vents as model ‘untouched’ ecosystems for developing ecological theory for conservation, advocating the potential of a vent trait database. Next, we built a global trait database for vent species – ‘sFDvent’. We selected traits that characterized the performance of a species and its contribution to ecosystem function, and best matched with established trait databases to ensure cross-ecosystem consistency. An international pool of experts scored these traits to populate the 14-trait database. Using sFDvent, we: created the first map of functional biogeography for deep-sea hydrothermal vents; assessed global-scale functional biodiversity trends (e.g., the East Pacific has fewer functionally unique species than the West Pacific, based on preliminary analyses); and evaluated the potential roles of large-scale environmental processes on these patterns.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".