Does fish larval dispersal differ between high and low latitudes?
Bibliographic record
Abstract
Several factors lead to expectations that the scale of larval dispersal and population connectivity of marine animals differs with latitude. We examine this expectation for demersal shorefishes, including relevant mechanisms, assumptions and evidence. We explore latitudinal differences in (i) biological (e.g. species composition, spawning mode, pelagic larval duration, PLD), (ii) physical (e.g. water movement, habitat fragmentation), and (iii) biophysical factors (primarily temperature, which could strongly affect development, swimming ability or feeding). Latitudinal differences exist in taxonomic composition, habitat fragmentation, temperature and larval swimming, and each difference could influence larval dispersal. Nevertheless, clear evidence for latitudinal differences in larval dispersal at the level of broad faunas is lacking. For example, PLD is strongly influenced by taxon, habitat and geographical region, but no independent latitudinal trend is present in published PLD values. Any trends in larval dispersal may be obscured by a lack of appropriate information, or use of 'off the shelf' information that is biased with regard to the species assemblages in areas of concern. Biases may also be introduced from latitudinal differences in taxa or spawning modes as well as limited latitudinal sampling. We suggest research to make progress on the question of latitudinal trends in larval dispersal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".