Optimization methodology for a river temperature monitoring network for the characterization of fish thermal habitat
Why this work is in the frame
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Bibliographic record
Abstract
A methodology for planning an optimized river water temperature monitoring network is presented. The methodology is based on sampling of the physio-climatic variability of the region to be monitored. Physio-climatic metrics are selected to describe the study region, based on principal component analysis. The sites to be monitored are then identified based on a k-means clustering in the multidimensional space defined by the selected metrics. The methodology is validated on an existing dense water temperature network in Haute-Savoie, France. Different configurations of more or less dense network scenarios are evaluated by assessing their ability to estimate water temperature indices at ungauged locations. An optimized network containing 83 sites is found to provide satisfactory estimations for seven ecologically and biologically meaningful thermal indices defined to characterize brown trout thermal habitat.
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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 | 0.002 | 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 | 0.001 |
| 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 it