Quantifying the “Bio-” Components in Biophysical Models of Larval Transport in Marine Benthic Invertebrates: Advances and Pitfalls
Bibliographic record
Abstract
Biophysical models are being used increasingly, both as predictive tools of larval dispersal for a particular system and for general evaluation of the role of different factors in larval transport. In the results of such models, larval duration, mortality, and behavior in the water column have exhibited pronounced effects on larval dispersal of marine benthic invertebrates. The parameterization of these processes has broadly reflected values from laboratory experiments, but the accuracy of these values is unknown. The pelagic larval duration used in models should be determined by laboratory, or preferably field, studies and should incorporate environmentally dependent variability. For mortality, in situ estimates are now feasible and, likely, more accurate than the currently used values. Larval behavior can be measured in the field, by high-frequency sampling of distributional changes relative to features in the water column or by controlled larval releases in tractable systems. To successfully validate the outcomes of these models, we must either improve our techniques for measuring larval abundance at the end of larval transport immediately before settlement, or incorporate components for settlement into the models. We must also address the mismatch in sampling resolution between biological and physical processes. If used with caution, this powerful approach can significantly advance our understanding of larval transport.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".