Peer Review #3 of "Interactions between two functionally distinct aquatic invertebrate herbivores complicate ecosystem- and population-level resilience (v0.1)"
Bibliographic record
Abstract
Resilience, the capacity for a system to bounce-back after a perturbation, is critical for conservation and restoration efforts.Different functional traits have differential effects on system-level resilience.We test this experimentally in a lab system consisting of algae consumed by zooplankton, snails, or both, using an eutrophication event as a perturbation.We examined seston settlement load, chlorophyll-a and ammonium concentration as gauges of resilience.We find that Daphnia magna increased our measures of resilience.But this effect is not consistent across ecosystem measures; in fact D.magna increased the difference between disturbed and undisturbed treatments in seston settlement loads.We have some evidence of shifting reproductive strategy in response to perturbation in D.magna and in the presence of Physa sp..These shifts correspond with altered population levels in D.magna, suggesting feedback loops between the herbivore species.While these results suggest only an ambiguous connection between functional traits to ecosystem resilience, they point to the difficulties in establishing such a link: indirect effects of one species on reproduction of another and different scales of response among components of the system, are just two examples that may compromise the power of simple predictions.
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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 | 0.011 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.274 | 0.169 |
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".