Long-term changes in Baltic herring (<i>Clupea harengus membras</i>) growth in the Gulf of Finland
Bibliographic record
Abstract
We used the longest available weight-at-age (WAA) time series (from 1950 to 1999) for Baltic herring (Clupea harengus membras L.) in the Gulf of Finland to investigate which environmental factors affect Baltic herring growth. The relationships among WAAs, annual weight increments, and growth rates for different herring year classes, water salinity, temperature, zooplankton abundance, and biomass, as well as stock sizes of herring, sprat, and cod, were evaluated. Our results showed that in the Gulf of Finland, herring weight and growth rate correlated positively with salinity, and WAA correlated positively with the abundance of the marine zooplankton species Pseudocalanus minutus elongatus. A density-dependent mechanism was not likely to explain the changes in herring WAA in the Gulf of Finland, because no significant correlation between herring WAAs and herring abundance could be found. Instead, the zooplankton community structure changed during the research period, which supports the theory of bottom-up controlling mechanism. A strong negative correlation between herring weight and sprat biomass may indicate competition for food between these species.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".