Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set
Bibliographic record
Abstract
Using a ∼ 1000 lake data set that spans the entire continental United States, we applied empirical modeling approaches to quantify the relative strength of nutrients and water temperature as predictors of cyanobacterial biomass (CBB). Given that cyanobacteria possess numerous traits providing competitive advantage under warmer conditions, we hypothesized that water temperature, in addition to nutrients, is a significant predictor of CBB. Total nitrogen (TN), water temperature, and total phosphorus were all significant predictors of CBB, with TN explaining the most variance. Using multiple linear regression analysis, we found that TN and water temperature provided the best model and explained 25% of the variance in CBB. However, when the data set was divided according to basin type, these same variables explained a higher amount of the variation in deep natural lakes (33%, n = 253), whereas the least amount of variation was explained by these variables in shallow reservoirs (12%, n = 307). Competing path models on the full data set using the best variables selected by multiple linear regression show that nitrogen and temperature are indirectly linked to cyanobacteria by association with total algal biomass, which likely reflects changes in light climate and other secondary factors. Our models also indicated that temperature was linked to cyanobacteria by a direct pathway. Under a scenario of atmospheric CO2 doubling from 1990 levels (resulting in an estimated 3.3°C increase of the maximum lake surface water), we predict on average a doubling of CBB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".