Evaluation of contributions of stock abundance and global temperature anomaly to mean body weight of pacific salmons in the North Pacific basin
Bibliographic record
Abstract
Dynamics of stocks is considered for pacific salmons in the North Pacific in 1925-2015 on the data from Russia, Japan, Canada, and the USA. The mean body weight dependence on the stock values and global air and sea surface water temperature is examined and evaluated by means of multi-dimensional regression analysis for the period 1961-2015. For many cases, interannual dynamics of the mean body weight depends strongly on combined effect of the corresponding stocks fluctuations and change of the global temperature anomaly index for the North Hemisphere: the multiple regression coefficient is R > 0.6 for 40 % of the tested time series. The highest correlation is found for chum and chinook salmons from Russia, pink and chinook salmons from Alaska, chum and sockeye salmons from Canada, and pink and chum salmons from Washington, Oregon, and California (Japanese data on body weight aren’t analyzed because of drift nets selectivity). Dominant predictors correlate negatively with the body weight for all species. Contribution of the global temperature index prevails for approximately 70 % of the rows. The stock is more important for pink and sockeye salmons from Russia and Alaska and in the former case the temperature index has positive correlation with the body weight. The relationships do not reflect direct influence of stocks or temperature on the weight but are presumably based on adaptive response of the populations to changes of feeding conditions for optimization of the stocks. This mechanism is confirmed by such temporal pattern as sub-trend dynamics typical for fluctuations of the salmons mean body weight, that means that this parameter demonstrates a tendency to long-term increasing or decreasing, changing periodically. Recent level of the body weight and catch indicates sustainable feeding conditions for pacific salmons in the North Pacific, at least in the last two decades, that unfortunately cannot be confirmed for the whole region by direct observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".