Effects of humic stress on the zooplankton from clear and <scp>DOC</scp>‐rich lakes
Bibliographic record
Abstract
Summary Humic stress is associated with the widespread and ongoing browning of lakes. Natural landscape gradients in dissolved organic carbon (DOC) potentially result in aquatic communities with different tolerances to humic substances and thus expected contrasting responses to further lake browning. If zooplanktonic species are adapted to different background concentrations of DOC, then we expected that the zooplankton from naturally DOC‐rich lakes would maintain higher diversity, biomass and overall density in the face of experimental browning than the zooplankton from DOC‐poor lakes. We tested this hypothesis in a common‐garden experiment by exposing, in enclosures, zooplankton from replicate DOC‐rich and DOC‐poor source lakes to simulated browning and to clear water. We conducted a 2 × 2 × 3 factorial‐design field transplant experiment with zooplankton from replicate DOC‐rich (>8.5 mg L−1) and DOC‐poor (<3.5 mg L−1) lakes (Québec, Canada) over eight weeks. There were two fixed effects: water treatment (brown or clear water) and zooplankton source (from DOC‐rich or DOC‐poor lakes). Lake source was included as a random variable in the model for the response of copepod body size in the enclosures. A substance derived from peat, ‘SuperHume’, was used as a source of DOC. The diversity, biomass and total density of zooplankton from DOC‐rich and DOC‐poor lakes did not differ upon experimental addition of further DOC. This was despite the presence of different copepod body size phenotypes between source lakes that could have potentially caused different community responses: several dominant species of copepods (Cyclops scutifer, Leptodiaptomus minutus and Tropocyclops prasinus mexicanus) had a larger mean population body size in DOC‐rich source lakes than in DOC‐poor source lakes. Our findings suggest that the zooplankton from DOC‐rich lakes does neither better nor worse than zooplankton from DOC‐poor lakes when faced with browning from a humic stressor.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".