Variability analysis of the transitory climate regime as defined by the NDVI/T/sub s/ relationship derived from NOAA-AVHRR over Canada
Bibliographic record
Abstract
This research work outlines an original method for climate observation by remote sensing based on the local combination of normalized difference vegetation index (NDVI) and land surface temperature (T/sub s/) measurements acquired by the NOAA-AVHRR sensor. It explores the phenomenon of linearity observed between T/sub s/ and the NDVI, which varies from positive to negative according to the conditions of the land surface energy budget regime and the vegetation type. Over vegetation, the decreasing relationship of T/sub s/ in relation to the NDVI (negative regression) due to vegetation cover transpiration is well known. However, over soils with sparse vegetation, bare soil, lichens or tundra, the relationship is reversed (positive regression) due to the high surface albedo which influences T/sub s/ values. The method is first demonstrate using full spatial and temporal resolution HRPT images over the BOREAS area corrected for atmospheric effects and screened for cloud cover in comparison with temperature and precipitation data. The method is then applied to composite images from the PAL multi-annual database at a resolution of 8 km and for Canada overall. It permits the determination of the ecotone position separating the forest from the tundra and the monitoring of the inter-annual fluctuations related to climatic variations and global warming.
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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 | 0.001 |
| 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) | 0.000 | 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 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".