Phytoplankton productivity across prairie saline lakes of the Great Plains (USA): a step toward deciphering patterns through lake classification models
Bibliographic record
Abstract
We investigated patterns of primary production across prairie saline lakes in the central and northern Great Plains of the United States. Based on comparative lake sampling in 2004, seasonal predictors of algal primary productivity were identified within subsets of similar lakes using a combination of Akaike’s information criterion (AIC) and classification and regression trees (CART). These models indicated complex patterns of nutrient limitation by nitrogen (N), phosphorus (P), and iron (Fe) within different lake groups. Nutrient enrichment assays (control, + Fe, + N, + P, + N + P) were performed in spring and summer of 2006 to determine if phytoplankton in selected lakes followed predicted patterns of nutrient limitation. Both the comparative lake sampling and experimental results indicated that N limitation was widespread in these prairie lakes, with evidence for secondary P limitation in certain lakes. In the experiments, iron did not stimulate primary production. Our results suggest that given the diverse geochemical nature of these lakes, classification models that separate saline lakes into subsets may be an effective method for improving predictions of algal production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".