High-resolution assessment and visualization of environmental stressors in the Lake Superior basin
Bibliographic record
Abstract
Quantifying gradients of anthropogenic stress can inform the development of sample designs, provide an important covariate in modeling relationships of response variables, identify reference and highly-disturbed sites, and provide a baseline and guidance to restoration and remediation efforts. We describe development of SumRel, a composite index of anthropogenic stress, for the U.S. and Canadian Lake Superior basin. Key elements of the project include development of high-resolution watersheds throughout the basin, summarization of the major point and non-point stressors within these watersheds, and creation of tools for scaling the watersheds and stressor summaries. SumRel was calculated at two spatial scales: for high resolution subcatchments within the Lake Superior basin (mean watershed area = 93 ha) and for coastal watersheds of Lake Superior. An assessment of subcatchments within Minnesota's St. Louis River watershed showed a correlation between the degree of disturbance, as indicated by SumRel, and impaired water quality, as evidenced by in-stream conductivity. These data and tools allow identification and visualization of reference and highly-disturbed sites at multiple spatial scales, providing decision support for individual agency and binational monitoring, assessment and restoration initiatives across the Lake Superior basin.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".