Comparing predictive cyanobacterial models from temperate regions
Bibliographic record
Abstract
The global increase in cyanobacterial bloom reports heightens the need for a critical evaluation of models used for their prediction. In particular, it is unclear whether empirical cyanobacterial models vary regionally because of differences in environmental conditions and (or) community composition. To address this question, we applied linear and nonlinear models as well as mixed-effect models to a dataset of seasonally integrated environmental and cyanobacterial measurements collected from 149 lakes spread across three regions in Canada. Across all lakes, we found that linear models outperformed nonlinear approaches and that nutrients (phosphorus, nitrogen) were the best predictors of cyanobacterial biomass. Importantly, there was no significant regional difference in predicted cyanobacterial responses to nutrients, even though the means for these variables were different among regions. From canonical correspondence analyses of taxonomic biomass data, temperature, water column stability, and forms of inorganic nitrogen were also important in explaining cyanobacterial community structure at the regional scale. Based on these analyses, we conclude that North American models are suitable for estimating total cyanobacterial biomass from any particular temperate region in Canada.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".