Insights from genetic and demographic connectivity for the management of rays and skates
Bibliographic record
Abstract
Studying demographic and genetic connectivity can help assess marine metapopulation structure. Rays and skates have no larval phase; hence, population connectivity can only result from active movement of individuals. Using thornback ray (Raja clavata) in European waters as a case study, demographic and genetic connectivity were studied for 11 putative populations with unequal population abundances and two hypotheses of dispersal rates. Genetic simulation results highlighted three large metapopulations: in the Mediterranean, around the Azores, and on the Northeast Atlantic shelf. Demographic results highlighted a finer population structure indicating that several pairs of putative populations might be demographically linked. Results were highly sensitive to dispersal assumptions and relative population abundances, which provided insights into the potential magnitude of genetic and demographic connectivity differences. Accounting for demographic connectivity appears to be crucial for managing and conserving rays and skates, while genetic connectivity provides a longer-term perspective and less subtle spatial structures. Moreover, accounting for heterogeneity in population abundances is a key factor for determining or interpreting metapopulation connectivity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".