The Effects of Salinity Toxicity on Species from a Sub-Arctic Zooplankton Community, in Light of a Changing Environment
Bibliographic record
Abstract
Increasing temperatures are expected to cause secondary salinisation in freshwater systems, such as the zooplankton community of Churchill, Manitoba. Salinity is a key environmental factor structuring these communities, thus, increasing salinity should cause these communities to change; however, previous research has shown that they are resilient. In order to ascertain how changing salinity will affect these communities I conducted toxicity tests for five crustacean species, individually and in a community setting. I sampled several pools throughout the season to ascertain the salinity level at which those same species were most abundant in the field. While the species had significantly different field distributions along the salinity gradient, the salinity levels associated with maximum densities did not correlate with the lab tolerances. However, lab tolerances were outside the field salinity range, thus providing an unexpected result. Local interactions appear to be very important in determining final community composition along this salinity gradient.
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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 | 0.000 | 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) | 0.001 | 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".