Zooplankton biomasses from 624 lakes across Canada
Bibliographic record
Abstract
We examined crustacean zooplankton biomass in 624 freshwater lakes spanning 12 ecozones within 6 continental drainage basins as part of the NSERC Canadian Lake Pulse Network project. Lakes were sampled once, over three summers (2017-2018-2019). Zooplankton were collected at the deepest point of each lake using a 100μm mesh Wisconsin net. Zooplankton were anesthetized with CO2 (Alka-Seltzer) and samples were preserved at room temperature in 70% ethanol. Samples were identified to the species level by BSA Environmental Services (Ohio, U.S.A.) using a dissecting microscope (100x to 400x magnification). Species biomasses were calculated by BSA using the mean length of up to 10 measured individuals per taxon per lake (Beisner et al., 2021), and known size/weight relationships for each species (Dumont et al., 1975; McCauley, 1984; Lawrence et al., 1987). The file “zooplankton biomass” contains the biomasses (µg d.w./L) of 102 crustacean zooplankton taxa from 624 lakes across Canada. The file “lake location” contains the location (longitude and latitude coordinates) and respective Ecozone and Continental Basin allocations of the 624 lakes. If you use these data, please cite:Paquette, C., I. Gregory-Eaves, and B. E. Beisner. 2021. “Multi-Scale Biodiversity Analyses Identify the Importance of Continental Watersheds in Shaping Lake Zooplankton Biogeography.” Journal of Biogeography 48: 2298–2311. doi.org/10.1111/jbi.14153
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".