Thermal features of the Mackenzie basin from NOAA AVHRR observations for summer 1994
Bibliographic record
Abstract
A series of mid‐afternoon Advanced Very High Resolution Radiometer (AVHRR) thermal radiance scenes were assembled in order to develop a better understanding of the complex energy and water processes leading to variations in surface temperature. An in‐depth knowledge of the temperature variability is of interest to land surface process modelling and its application to the Mackenzie Global Energy and Water Cycle Experiment (GEWEX) Study (MAGS). Clear‐sky land surface temperatures are estimated by applying a split window technique to remove atmospheric effects. A maximum land surface temperature map of the Mackenzie basin at 1‐km scale for summer 1994 is produced. The patterns are related to land surface features and elevation. The basin's maximum land surface temperature patterns can be subdivided into three land zones (≥ 35°C, 33–34°C and 27–32°C) and a water dominated zone (20.5°C on average). The highest maximum temperature zone (≥35°C) corresponds to a combination of minimal vegetation, drier soils and low terrain. This zone is not in the southern part of the basin as might be speculated in the absence of these data, but in a wide low elevation corridor from west of Great Bear Lake along the Mackenzie River down to 50°N, 120°W. The maximum land surface temperatures tend to decrease with increasing vegetation density and surface moisture; they also decrease with elevation at a rate of –4.5°C km–1. This is confirmed by weather station data. The AVHRR data extend this relationship to the 1200 – 2200 m altitude ranges, where there are no station data. The data suggest that elevation and land cover should be taken into account in the objective analysis (spatial interpolation) of station data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".