Estimation of litter mass in nongrowing seasons in arid grasslands using MODIS satellite data
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".