Short to medium-term forecasting of fishing ground distribution based on deep learning model
Bibliographic record
Abstract
Most of the fishing ground research focuses on real-time predictions and lacks continuous forecasting for future changes over a certain period. Traditional models characterized by large, single temporal scales lack effectiveness in accounting for the autocorrelation of environmental factors. Deep learning has demonstrated superior performance and promising development prospects due to its accurate and efficient ability to mine nonlinear information in the era of big data. Therefore, we take Ommastrephes bartramii as an example and constructed 28 different temporal scales and lead periods cases based on U-Net and compared them with GAM, NN and ConvLSTM model results. The input factors of this model are sea surface temperature (SST) and the output factors are the center fishing ground data (1998-2019). The results reveal that the optimal temporal scale and lead period for this model is 15 days and 4 on U-Net. The SST fluctuation information between different lead periods of environmental field and the coupling degree in fishing grounds may be essential factors affecting the model performance differences. It enhances marine fisheries understanding from artificial intelligence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".