Species–energy relationships in deep-sea molluscs
Bibliographic record
Abstract
Consensus is growing among ecologists that energy and the factors influencing its utilization can play overarching roles in regulating large-scale patterns of biodiversity. The deep sea--the world's largest ecosystem--has simplified energetic inputs and thus provides an excellent opportunity to study how these processes structure spatial diversity patterns. Two factors influencing energy availability and use are chemical (productive) and thermal energy, here represented as seafloor particulate organic carbon (POC) flux and temperature. We related regional patterns of benthic molluscan diversity in the North Atlantic to these factors, to conduct an explicit test of species-energy relationships in the modern day fauna of the deep ocean. Spatial regression analyses in a model-averaging framework indicated that POC flux had a substantially higher relative importance than temperature for both gastropods and protobranch bivalves, although high correlations between variables prevented definitive interpretation. This contrasts with recent research on temporal variation in fossil diversity from deep-sea cores, where temperature is generally a more significant predictor. These differences may reflect the scales of time and space at which productivity and temperature operate, or differences in body size; but both lines of evidence implicate processes influencing energy utilization as major determinants of deep-sea species diversity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".