Bibliographic record
Abstract
Abstract. With increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, the gradient boost regressor (GBR) and long short-term memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements and the others applying a two-step approach, first estimating lake nutrients that have limited observations and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training–testing year combinations.
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 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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.101 | 0.063 |
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".