Recruitment variability in Baltic Sea sprat (<i>Sprattus sprattus</i>) is tightly coupled to temperature and transport patterns affecting the larval and early juvenile stages
Bibliographic record
Abstract
Recruitment patterns of Baltic Sea sprat (Sprattus sprattus) were correlated to time series of (i) month- and depth-specific temperature conditions and (ii) larval drift patterns inferred from long-term Lagrangian particle simulations. From the latter, we derived an index that likely reflected the variable degree of annual larval transport from the central, deep spawning basins to the shallow coastal areas of the Baltic Sea. The drift index was significantly (P < 0.001) correlated to sprat recruitment success and explained, together with sprat spawning stock biomass, 82% of the overall variability between 1979 and 2003. Years of strong larval displacement towards southern and eastern Baltic coasts corresponded to relative recruitment failure, while years of retention within the deep basins were associated with relative recruitment success. The strongest correlation between temperature and recruitment occurred during August in surface waters, explaining 73% of the overall variability. Together, the two approaches advocate that new year classes of Baltic sprat are predominantly composed of individuals born late in the season and are determined in strength mainly by processes acting during the late larval and early juvenile stages. However, prior to be included in recruitment predictions, the biological mechanisms underlying these strong correlations may need to be better resolved.
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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".