Beyond Climate Warming: How Salinization Accelerates Deoxygenation in Lakes
Bibliographic record
Abstract
This dataset provides a harmonized collection of lake- and watershed-scale attributes, time series, and analysis code used to evaluate salinization-driven deoxygenation in lakes across Canada and the contiguous United States. It includes a lake attributes table for 144 study lakes (coordinates, morphometry, residence time, stratification metrics such as BVF(t,S), and deep-water chemistry including hypolimnetic Cl/SC and DO), together with linked watershed descriptors derived from HydroLAKES, GLOBathy, BasinATLAS and global land-use/land-cover products (population density, fraction urban, road density, watershed-to-lake area ratio, climate statistics, and snow cover). Annual mean hypolimnetic DO and salinity proxies are provided for each lake for 1988–2022, along with a companion time-series_plots folder (zip file) containing 144 lake-specific JPG figures that visualize these trends. A separate table lists HydroLAKES waterbodies in Canada and the United States predicted to be at risk of salinization-driven deoxygenation based on the logistic-regression framework described in the associated manuscript. The repository also includes the core Python scripts used for data pre-processing, geospatial extraction, clustering, statistical analyses, and figure generation, enabling users to reproduce and extend the workflows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.002 |
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".