STREAM AND RIPARIAN TEMPERATURES IN THE NICOLA RIVER WATERSHED, BRITISH COLUMBIA, CANADA
Bibliographic record
Abstract
Airborne thermal remotely sensed images of riparian and water surface temperatures were acquired at 12 sites in the Nicola River watershed of south-central British Columbia, Canada, using a forward-looking infrared (FLIR) camera. Ground-truth observations to correlate radiant (T r ) versus kinetic (T k ) water temperatures were performed at 3 sites and showed an accuracy of ±0.4°C. Landscape and water thermograms obtained at 3 representative sites in the study area were analyzed and revealed apparent thermal landscape-water interactions contributing to the observed spatial heterogeneity in stream temperatures. However, a critical analysis of remotely sensed stream heating patterns revealed that approximated solar energy inputs and conduction from adjacent streambanks and the atmosphere could only account for ca. 0.5% of the apparent required heat influx in some locations, suggesting imaging interference by emissive radiation from the exposed land surfaces. Pixel mixing of land and water surface temperatures was also found to be a potential interferant in narrow braided channels with widths near the resolution of the camera (0.15-0.5 m). The utility of the method for assessing mixing in and between riverine systems was also shown. Overall, aerial remote sensing of stream and riparian surface temperatures appears to be a promising technology for assessing spatial heterogeneity, and may be useful in conjunction with conventional in-stream methods as part of a hybrid spatial-temporal observing system for aquatic management, provided further work is performed to validate observed temperatures near exposed streambanks, in vegetation shadows, and other areas where emissive interference may be problematic. AIRBORNE THERMAL INFRARED REMOTE SENSING OF
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".