Planktonic linkages among marine protected areas on the south Florida and southeast United States continental shelves
Bibliographic record
Abstract
One proposed benefit of marine protected areas (MPAs) is increased larval export, potentially increasing recruitment in unprotected areas. Because most marine species have planktonic larvae, information regarding planktonic transport is needed to evaluate the benefit of larval export. We used satellite-tracked drifters to define planktonic transport routes and rates from three MPAs along the south Florida and southeast United States (US) continental shelves. Drifter tracks indicated both long-distance transport and local retention. A probability model was developed based on drifter releases. The region was broken into zones; zone-specific residence times and movements between zones were defined from the drifter tracks. Transport out of the region in association with the Loop Current – Florida Current – Gulf Stream was the most frequently observed outcome, yet retention was high in the lower Florida Keys and on the Georgia shelf. From the model results, long-distance planktonic transport and local retention are the endpoints of a continuum rather than a dichotomy. Further, the outcome of planktonic transport is spatially heterogeneous with some regions exhibiting more retention and others exhibiting more export. The spatial aspects of planktonic transport described here should be considered in designing MPAs with fishery management objectives in the south Florida and southeast US shelf ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".