Zooplankton functional trait database for Canadian lakes
Bibliographic record
Abstract
This dataset contains functional traits of 102 crustacean zooplankton taxa sampled in 624 Canadian lakes (Pelagic sample), as well as 58 sub-fossil cladoceran taxa (Sediments sample) sampled in 101 lakes. Lakes were sampled across Canada as part of the NSERC Canadian LakePulse Network. The traits used are: feeding type (B(Bosmina)-filtration, C(Chydorus)-filtration, D(Daphia)-filtration, S(Sidae)-filtration, stationary suspension or raptorial), habitat (littoral, pelagic or intermediate) and trophic group (carnivore, herbivore, omnivore, or a combination of these). Pelagic species length of up to 10 individuals per taxon per lake were measured by BSA Environmental Services (Ohio, U.S.A.), and averaged for each taxon. Sediments sample lengths were either obtained from the literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Griffiths et al., 2019), or from the Pelagic sample length data. Feeding type, habitat and trophic group functional traits were obtained from literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Hébert et al., 2016; Griffiths et al., 2019). References Barnett, A. J., Finlay, K., & Beisner, B. (2007). Functional diversity of crustacean zooplankton communities: Towards a trait-based classification. Freshwater Biology, 52(5), 796–813. https://doi.org/10.1111/j.1365-2427.2007.01733.x Demott, W. R., & Kerfoot, W. C. (1982). Competition among cladocerans: nature of the interaction between Bosmina and Daphnia. Ecology, 63(6), 1949–1966. https://doi.org/10.2307/1940132 Griffiths, K., Winegardner, A. K., Beisner, B. E., & Gregory-Eaves, I. (2019). Cladoceran assemblage changes across the Eastern United States as recorded in the sediments from the 2007 National Lakes Assessment, USA. Ecological Indicators, 96, 368–382. 061 Hébert, M.-P., Beisner, B. E., & Maranger, R. (2016). A compilation of quantitative functional traits for marine and freshwater crustacean zooplankton. Ecology. https://doi.org/10.1890/15-1275
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".