Peer Review #1 of "Mediterranean swordfish (Xiphias gladius Linnaeus, 1758) population structure revealed by microsatellite DNA: genetic diversity masked by population mixing in shared areas (v0.1)"
Bibliographic record
Abstract
Background.The Mediterranean swordfish stock is overfished and considered not correctly managed.Elucidating the patterns of the Mediterranean swordfish population structure constitutes an essential prerequisite for effective management of this fishery resource.To date, few studies have investigated intra-Mediterranean swordfish population structure, and their conclusions are controversial. Methods.A panel of 20 microsatellites DNA was used to investigate fine-scale population structuring of swordfish from six main fishing areas of the Mediterranean Sea.Results.This study provides evidence to reject the hypothesis of a single swordfish population within the Mediterranean Sea.DAPC analysis revealed the presence of three genetic clusters and a high level of admixture within the Mediterranean Sea.Genetic structure was supported by significant F ST values while mixing was endorsed by the heterozygosity deficit observed in sampling localities indicative of a possible Wahlund effect, by sampling admixture individuals .Overall, our tests reject the hypothesis of a single swordfish population within the Mediterranean Sea.Homing towards the Mediterranean breeding areas may have generated a weak degree of genetic differentiation between populations even at the intrabasin scale.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.272 | 0.150 |
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".