Effects of whole-lake manipulations of nutrient loading and food web structure on planktonic respiration
Bibliographic record
Abstract
We assessed planktonic respiration in whole-lake manipulations of nutrient loading and food web structure in three manipulated and one unmanipulated lake over 7 years. The manipulations created strong contrasts in zooplankton body size across a series of nutrient loads. Large-bodied zooplankton were suppressed by planktivorous fish in one lake, while in the other two manipulated lakes, large-bodied zooplankton dominated community biomass. Nutrients were added as inorganic N and P. Nutrient loads ranged from background to conditions resembling eutrophic lakes. Planktonic respiration was measured weekly in each lake by dark bottle oxygen consumption. Respiration was low when lakes were not fertilized (average 8.5 µmol O2·L-1·day-1) and was correlated with differences in dissolved organic carbon among the lakes. Respiration increased with nutrient addition to a mean range of 12-25 µmol O2·L-1·day-1; however, respiration differed among lakes at the same nutrient loading. Further, respiration was independent of dissolved organic carbon in the fertilized lakes. Differences in the intensity of zooplankton grazing as determined by food web structure strongly regulated primary and bacterial production across the range of nutrient loads. Consequently, respiration was positively related to primary production, phytoplankton biomass, and bacterial production and inversely related to the average size of crustacean zooplankton.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".