Estimation of litter mass in nongrowing seasons in arid grasslands using MODIS satellite data
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
Litter has a special ecological functioningin grasslands. Few studies have been conducted to estimate litter mass using remotely sensed data during nongrowing seasons in arid grasslands although it is important forage for livestock sustainability. With MODIS data, estimation methods were developed for litter mass in the desert steppe of Inner Mongolia calibrated with field surveys. As MODIS Band 7 is located in the lignocellulose absorption pit of litter near 2100 nm, the best models were obtained for NDTI (normalized difference tillage index) (normalized difference between Bands 6 and 7) and STI (soil tillage index) (ratio of Band 6–7) among soil-unadjusted indices, and for MSACRI (modified soil-adjusted crop residue index) (modification of NDTI by incorporating soil line) among soil-adjusted indices. NDTI and STI explained 63% of the variance of litter mass, while MSACRI explained 71% of the variance. If data are not available for calculating soil line, it may be appropriate to use the soil-adjusted NDTI (S-NDTI), a new index proposed in the study that incorporates a soil adjustment factor into the NDTI equation. The optimal S-NDTI explained 66% of the variance. The NDTI, STI, MSACRI and S-NDTI can be applied to estimate litter mass in arid grasslands.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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