Summer assessment of zooplankton biodiversity and environmental control in urban waterbodies on the Island of Montréal
Bibliographic record
Abstract
Abstract Waterbodies in urban environments are usually built or maintained to serve socioeconomic functions. However, they also represent ecosystems that can contribute notably to urban biodiversity. To better understand contribution to biodiversity of urban ecosystems, the variation in zooplankton community composition in 19 waterbodies across the Island of Montréal (Québec, Canada) was monitored across three summer months. Communities were dissimilar between and within waterbodies with species richness differences and replacement patterns playing equal parts in shaping the observed variation. Within each waterbody, notable differences were detected between months, which can affect biodiversity estimation or community composition assessment. Zooplankton species richness was especially well explained by macrophyte cover, which had a positive effect. Compositional differences were also explained by macrophyte cover and by waterbody emptying. Partitioning the beta diversity revealed that only richness difference patterns were explained by macrophyte cover, as species replacement patterns were not explained by any of the measured environmental variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".