Nutrient, chlorophyll, and water clarity relationships in Florida's nearshore coastal waters with comparisons to freshwater lakes
Bibliographic record
Abstract
Models relating chlorophyll to nutrients and Secchi depth to chlorophyll using data from nearshore coastal waters of Florida were successfully developed. The models suggest that phosphorus is the primary limiting factor for phytoplankton in the nearshore coastal waters of Florida and that total phosphorus concentration accounts for 81% of the variance in chlorophyll concentration. The models also show that chlorophyll is the dominant factor determining Secchi depth in nearshore coastal waters of Florida and that chlorophyll concentrations account for 68% of the variance in Secchi depth. Thus, these models are robust and should be useful for eutrophication management of Florida's coastal marine systems. The models developed with data from nearshore coastal waters of Florida are similar to models developed for freshwater lakes in Florida, but the amount of chlorophyll per unit of phosphorus and Secchi depth per unit of chlorophyll are both significantly less for marine samples. This suggests that the chlorophyll to biovolume ratios in the nearshore coastal waters of Florida are less than in freshwater systems of Florida. Therefore, nutrient to chlorophyll and chlorophyll to Secchi depth models developed for freshwater systems are probably ill suited for use in Florida's marine systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".