Data on zooplankton species and lake environments from 29 lakes in southern Ontario, Canada
Bibliographic record
Abstract
The data published here were used to determine temporal variability in the drivers of zooplankton community composition, and to identify associations between individual species and different environmental metrics. Here, we provide two datasets, which are used in the paper by Sinclair et al. titled: "Consistent and transient drivers of freshwater zooplankton communities". Each dataset is provided as a separate tab in a single Excel spreadsheet. The “Info” tab provides more detail on each. The first dataset ("Zooplankton community") provides the total abundance of each adult zooplankton species detected across 29 lakes from southern Ontario, Canada in 2013, 2014, 2015, and 2016. The second dataset ("Lake environments") contains information on the depth, area, and water chemistry of each sampled lake in each year. Collection methods and dates are detailed in the associated article and variable descriptions and units are included in the "Info" tab.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".