Ice cover exists: A quick method to delineate groundwater inputs in running waters for cold and temperate regions
Bibliographic record
Abstract
Abstract Groundwater can be important in regulating stream thermal regimes in cold, temperate regions, and as such, it can be a significant factor for aquatic biota habits and habitats. Groundwater typically remains at a constant temperature through time; that is, it is warmer than surface water in winter and cooler in summer. Further, small tributaries are often dominated by groundwater during low flows of winter and summer. We exploit these thermal patterns to identify and delineate tributary/groundwater inputs along a frozen river (ice‐on) using publically available satellite data, and we tested the findings against airborne, thermal infrared (TIR) data. We utilize a supervised maximum likelihood classification (sMLC) to identify possible groundwater inputs while the river is in a frozen state (kappa coefficient of 96.77 when compared with visually delineated possible groundwater inputs). We then compare sMLC‐identified possible groundwater inputs with TIR‐classified groundwater inputs, which confirmed that there was no statistical difference (χ2 = .78), that is, confirming that groundwater inputs can be delineated in north temperate river systems using available satellite imagery of the system's frozen state. Our results also established the spatial extent and influence of possible groundwater inputs in two seasons. The thermal plumes were longer and narrower in winter; this is likely related to seasonal differences in dispersion regimes. We hypothesize that differences between summer and winter is related to either (a) tributaries that are modulated by shading in summer or (b) aquifer disconnection from the river in winter owing to frozen ground conditions and lack of aquifer recharge. This method of establishing tributary/groundwater inputs and contributions to surface water thermal regimes is relatively simple and can be useful for science and management as long as “ice cover exists”; that is, the system can achieve a frozen state.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".