Effects of light and nutrients on the net accumulation and elemental composition of epilithon in boreal lakes
Bibliographic record
Abstract
1. Two experiments in the Experimental Lakes Area (ELA) in north‐western Ontario, Canada examined the effects of light and two key elements on the net accumulation and elemental composition of epilithon. In Lake (L) 224, benthic algae were grown under different light intensity and phosphorus supply, while in L302S we provided three levels of two different carbon sources (bicarbonate and glucose) to algae colonizing nutrient‐diffusing substrata. After 1 month of accumulation, we sampled biofilms for chlorophyll (chl), carbon (C), phosphorus (P) and algal C. 2. Increased C supply did not significantly affect algal growth (C or chl) or elemental composition (C/P ratios) in L302S. However, P enrichment increased chl and algal C, dramatically reduced the C/P ratio of epilithon, and did not affect total organic C in L224. Phosphorus enrichment also increased the proportion of algal material in the total particulate organic matter and altered the taxonomic composition of algae in L224 biofilms. Shading had no significant effect on the C/P ratio and total organic C in epilithon from the L224 experiment. 3. Our results demonstrate that P supply affects the elemental composition of organic matter that collects on rock substrata. It thus appears that low availability of P relative to C and light drives the formation and retention of high C/P organic matter on rock surfaces in oligotrophic boreal 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".