Beyond Climate Warming: How Salinization Accelerates Deoxygenation in Lakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it