Environmental drivers of herring growth and how the perception shifts with time series length
Bibliographic record
Abstract
Growth is a key component of population dynamics and, thus, fisheries management, yet drivers of its variations are often poorly understood. Using individual data collected over 80 years, we explored how environmental drivers affect growth in a major population of Atlantic herring (Clupea harengus). The results confirm that intrinsic factors (age and maturation) determine growth to a large degree but also that extrinsic factors such as temperature have some influence. While the role of intrinsic factors was independent of time series length, the importance of extrinsic drivers varies strongly with the analysed time period. It remains unclear whether this is caused by data inconsistencies back in time, spurious correlations appearing in shorter time series, shifts in population dynamics, or dynamic interactions between variables that cannot be determined with current data. Generally, environmental effects on growth became less clear and relevant with increasing time series length. What drives variation in growth may therefore change over time, potentially due to impacts such as fishing or climate change. It also underlines that seemingly clear correlations can break down or change their sign over time; hence, caution is advised when interpreting results from time series of 20–40 years.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| 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".