Total phosphorus–chlorophyll a size fraction relationships in southern Québec lakes
Bibliographic record
Abstract
The study is a first attempt to generate a series of Total Phosphorus–Chlorophyll a (TP–Chl a) predictiveOC models relating the quantitative responses of four algal size fractions to phosphorus gradients. The study was carried out in 27 glacial lakes from two regions in southern Quebec, the Laurentians and the Eastern Townships, and covered a relatively modest range of trophic conditions (TP, 3–34 µg P L‐1; Chl a, 0.3–7.6 µg L−1). Algal biomass was estimated using measurements of Chl a, and the total Chl a was divided into four operational size fractions: picophytoplankton <3 mm, nanophytoplankton 3–20 mm, nanophytoplankton plus picophytoplankton <20 mm (edible fraction), and microphyto‐plankton >20 mm (inedible fraction). We tested the hypothesis that the slopes of the TP–Chl a regression models developed for algal size fractions would increase consistently from the smallest to the largest algal size fraction, as suggested by the first half of the sigmoidal TP–Chl a models. Although there was no consistent trend in the magnitudes of the slopes of TP–Chl a relationships for picophytoplankton (slope = 1.14), nanophytoplankton (0.93), and microphytoplankton (1.22), Chl a concentrations in the largest size fraction increased more rapidly with phosphorus enrichment than in either of the smaller fractions. When included as an additional variable, lake water alkalinity improved the prediction of Chl a and presented differential effects on size fractions. The effect of TP enrichment on microphytoplankton is more pronounced in well‐buffered lakes, whereas TP enrichment has a stronger effect on nanophytoplankton in low‐alkalinity lakes. The effects of alkalinity may be the result of either a pH influence on phytoplankton carbon uptake or a stronger top‐down grazing effect on small algae in well‐buffered lakes.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".