Effects of Topographic Resolution and Geologic Setting on Spatial Statistical River Temperature Models
Bibliographic record
Abstract
Abstract River temperature exerts a critical control on habitat for aquatic biota. As the climate warms in eastern Canada, threats to habitats of cold‐water species will increase, underpinning the necessity to develop an understanding of landscape‐scale, thermal regimes of flowing waters. We assessed the performance of spatial statistical network (SSN) models of river temperature using high‐resolution thermal infrared imagery (0.6 m) and LiDAR (1 m) compared to NASA's Shuttle Radar Topography Mission (SRTM—30 m) topographic data and interrogate LiDAR derived fine‐scale models (3 ha) to describe groundwater connectivity to surface waters in catchments with shallow overburden and varied bedrock geology. LiDAR improved model performance in a catchment underlain by a homogeneous, high hydraulic conductance bedrock (Cains River) but did not improve model performance in a catchment with heterogeneous bedrock and variable hydraulic conductance (North Pole Stream). We hypothesize that differences in bedrock conductance modified topographic controls on subsurface flows and discharge patterns to the rivers and thus produced the mixed performance of the SSN models. At finer scales, river reaches in steep valleys incising high conductance bedrock produced groundwater discharge, which was absent in incised valleys with low conductance bedrock. These findings indicate that while topography exerts an important control on landscape‐scale hydrological processes, geologic setting is a similarly important influence on hydrological processes. We suggest the inclusion of a third dimension of spatial autocorrelation, representative of the vertical plane that captures the geologic setting, would broaden the geographic applicability of spatial statistical models for river temperature studies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".