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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".