Leaf area index estimation in semiarid mixed grassland by considering both temporal and spatial variations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Leaf area index (LAI) estimation in a mixed grassland ecosystem is limited by temporal and spatial variations controlled by land surface heterogeneity and ecological parameters. Therefore, simply estimated LAI usually has difficulty in meeting the requirements of the land surface–atmosphere interaction models. We estimated LAI based on the relationship between LAI and normalized difference vegetation index (NDVI) by considering temporal and spatial variations. Spatial variations of both LAI and NDVI were investigated using the Morlet wavelet approach. Based on the ground reflectance data, LAI estimation can be greatly improved by taking temporal and spatial variations into account. The coefficient of determination (r 2 ) values of the LAI-NDVI equations were increased by 0.28, 0.51, and 0.44 in the early, maximum, and late growing seasons, respectively. LAI estimation from SPOT 4/5 and Landsat TM 5 images confirmed the applicability of the proposed estimation approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it