Pelagic response of a humic lake to three years of phosphorus addition
Bibliographic record
Abstract
Three years of whole-lake phosphorus (P) fertilization, conducted in a 12-ha boreal forest lake, revealed significant changes in epilimnion nutrients, biomasses, and primary production. As a time average for all three treatment years, primary production increased 257% relative to the reference basin value of 16.5 mg·m3·day1, whereas the carbon masses of both nanoalgae (>2 µm) and zooplankton increased roughly 130% from their initial values of 15 mg·m3and 17 mg·m3, respectively. Calculated from a difference, the absolute increase in the sum of ciliates and picoplankton (heterotrophic bacteria and picoalgae) was more than six times as large as for algae larger than 2 µm, indicating that most added P ended in this compartment. Moreover, fertilization did not change the species inventory among nanoalgae and zooplankton, although the biomass composition changed somewhat. Only the former dominant species, the chrysophytes Dinobryon crenulatum, D. sociale v. americanum, Mallomonas allorgei, and Ochromonas sp. and the calanoid copepod Eudiaptomus gracilis increased substantially in biomass owing to the added P. Surprisingly, we observed a substantial delay in the food web response to fertilization, where most variables increased monotonously in size from year to year during the fertilization period. The underlying mechanisms for these delayed increases remains to be explained.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".