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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".