Predicting the vertical distributions of reef fish larvae in the Straits of Florida from environmental factors
Bibliographic record
Abstract
Three seasons of vertically stratified ichthyoplankton sampling at the edge of the Florida Current revealed consistent accumulations of some coral reef fish larvae under taxon-specific environmental conditions. Environmental variability ranging from predictable (seasonal differences in temperature, diel changes in light, and vertical gradients in many variables) to stochastic (changes in wind-driven turbulence and patchiness of zooplankton) was used to model larval distributions. In five taxa, including the commercially important Epinephelini (groupers), relative larval densities were predicted with significant accuracy based entirely on sampling depth. Models yielding these predictions were cross-validated among all seasons, indicating that larval vertical distributions were remarkably unaffected by other environmental factors, while revealing strong behavioral preferences for specific ranges of hydrostatic pressure. Pomacentridae (damselfish) larvae consistently occupied shallower depths at night than during the day, demonstrating diel vertical migrations. At the community level, depth and season were two major factors structuring larval coral reef fish assemblages. Predictable vertical distributions of larvae in the Straits of Florida can facilitate modeling the same taxa elsewhere in the Western Central Atlantic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".