Environmental variables measured in 85 lakes across Canada
Bibliographic record
Abstract
The file "env_variables_FWBpaper.csv" contains environmental variables from subset of 85 sampled as part of the NSERC Canadian Lake Pulse Network project over three summers (2017-2018-2019). Lake morphometry variables were either measured on site (lake depth), or obtained from HydroLAKES v. 1.0 (Messager et al. 2016). Water quality variables (water physical and chemical properties) were collected or measured at the deepest point of each lake, following the protocols from the NSERC Canadian Lake Pulse Network (2021). Watershed land use fractions were characterized for each lake, as described by Huot et al. (2019). Variables sampling depth and units are described in Paquette et al (2023). References Huot, Y., C. A. Brown, G. Potvin, and others. 2019. The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate. Sci. Total Environ. 695: 133668. doi:10.1016/j.scitotenv.2019.133668 Messager, M. L., B. Lehner, G. Grill, I. Nedeva, and O. Schmitt. 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7: 1–11. doi:10.1038/ncomms13603 NSERC Canadian Lake Pulse Network. 2021. NSERC Canadian Lake Pulse Network field manual 2017 - 2018 - 2019 surveys, M.-P. Varin, M.-L. Beaulieu, and Y. Huot [eds.]. Université de Sherbrooke. Paquette, C., Gregory-Eaves, I. et Beisner B.E. (2023) Congruence of water column, contemporary and pre-industrial sediment cladoceran assemblages in 85 Canadian lakes of contrasting human impact levels. Freshwater Biology, 00, 1-18.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.037 |
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; both teacher heads agree on what is shown here.
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".