Bootstrapped ANN for forecasting seawater chlorophyll-a around the north Pacific Rim
Bibliographic record
Abstract
Seawater chlorophyll-a (Chla) represents algal biomass in ocean and is a major index of eutrophication. In this paper, bootstrapped artificial neural network (BANN) model is developed for predicting the seawater Chla concentration around the north Pacific Rim. Three-layer ANN structure is applied and the modeling is based on comprehensive five-minute interval datasets of water temperature, depth, salinity and Chla collected by Continuous Plankton Recorder Survey of north Pacific in 2014. Prediction intervals (PI) are constructed according to the calculated uncertainties from the model structure and data noise. The performance of BANN is compared with traditional ANN model. The results show that BANN with 6 hidden neurons can forecast Chla effectively and produce better performance than ANN model when the bootstrapped number are bigger than 20. In addition, mean relative error (MRE) and root mean square error (RMSE) of the BANN(20) are respective 8.57% and 1.869 mg/m3, but the BANN is not fit for the Alaska coastal region. About 52 (1.31%) observations are larger than the upper bound of prediction intervals and these `outliers' fallen into three regions can be attributed to potential nutrients from mariculture or terrestrial influences which are inferred as anthropogenic and natural sources. The BANN model applied in this paper can help managers to make more appropriate decisions on ocean ecological management.
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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) | 0.001 | 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".