Predicting coastal eutrophication in the Baltic: a limnological approach
Bibliographic record
Abstract
Coastal eutrophication is a key environmental concern in Finland. A highly indented, well-settled coastline with a myriad of small estuaries means that eutrophication occurs at numerous localities. There is a clear need for general models that predict eutrophication across estuaries. Lake eutrophication has been successfully predicted using a combination of chlorophyll a (Chl) - total phosphorus (TP) regression models and TP mass-balance models. We applied this limnological approach to 19 Finnish estuaries. The Chl-TP regression was highly significant, accounting for 67% of the variation in Chl. When combined with a TP mass-balance equation, log observed and predicted Chl differed by 28% on average. Accuracy was improved by dividing the estuaries into those dominated by non-point-source (NPS) loading (n = 11) and those dominated by point-source (PS) loading (n = 7). A land-use regression model based on percentage of the catchment forested and estuarine mean depth then best predicted Chl in the NPS-dominated estuaries. The mass-balance approach remained the most accurate model for the PS estuaries. The land-use model and mass-balance approach are complementary tools in that their use maximizes accuracy for both NPS- and PS-dominated estuaries. This high level of accuracy demonstrates the relevance of limnological approaches to Finnish estuaries.
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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 | 0.000 |
| 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 | 0.001 |
| 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".