2. Effect of climate change on the predation rate of zooplankton in Swan Lake, Sudbury, ON
Bibliographic record
Abstract
Climate change interacts with other environmental stressors (e.g., acid deposition, calcium depletion, invasive species) to alter both the chemical and biological characteristics of Boreal Shield lakes, potentially leading to changes in aquatic biodiversity. Changes in biodiversity can result in loss of sensitive species and affect dynamic interactions among species at varying trophic levels. Currently, little is known about the effect of climate warming on predator-prey relationships in aquatic ecosystems. I examine how predicted warming of Boreal Shield lakes may affect predation rate. More specifically, my research examines how temperature affects the predation rate on zooplankton by common macroinvertebrate predators. Zooplankton, Chaoborus and Notonectidae were used from Swan Lake in Sudbury, ON. I performed 24-hr laboratory feeding trials to examine the rate at which predators feed over a range of natural and predicted lake temperatures. By investigating differences in invertebrate predation occurring in Swan Lake, we will be able to predict predator -prey relationships in Boreal Shield lakes subject to warming as a result of climate change.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".