Trophic-level determinants of biomass accumulation in marine ecosystems
Bibliographic record
Abstract
Metrics representative of key ecosystem processes are required for monitoring and\nunderstanding system dynamics, as a function of ecosystem-based fisheries management (EBFM).\nUseful properties of such indicators should include the ability to capture the range of variation in\necosystem responses to a range of pressures, including anthropogenic (e.g. exploitation pressures)\nand environmental (e.g. climate pressures), as well as indirect effects (e.g. those related to food\nweb processes). Examining modifications in ecological processes induced by structural changes,\nhowever, requires caution because of the inherent uncertainty, long feedback times, and highly\nnonlinear ecosystem responses to external perturbations. Yet trophodynamic indicators are able to\ncapture important changes in marine ecosystem function as community structures have been\naltered. One promising family of such metrics explores the changing biomass accumulation in the\nmiddle trophic levels (TLs) of marine ecosystems. Here we compared cumulative biomass curves\nacross TLs for a range of northern hemisphere temperate and boreal ecosystems. Our results confirm\nthat sigmoidal patterns are consistent across different ecosystems and, on a broad scale, can\nbe used to detect factors that most influence shifts in the cumulative biomass−TL curves. We conclude\nthat the sigmoidal relationship of biomass accumulation curves over TLs could be another\npossible indicator useful for the implementation of EBFM.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".