Summer-season Differences in NDVI and iTVDI among Vegetation Cover Types in Lake Mashu, Hokkaido, Japan Using Landsat TM Data
Bibliographic record
Abstract
The improved Temperature Vegetation Dryness Index (iTVDI) can be used as an indicator of transpiration rates in mountainous areas. We investigated the influence of vegetation cover types on differences observed in iTVDI, together with NDVI in vegetation covers around Lake Mashu in a summer day. Based on the results of comparing NDVI and iTVDI values among 14 vegetation cover types, it was shown that the vegetation cover type differences could cause significant differences in iTVDI values. Shrub and grassland categories showed lower NDVI but higher iTVDI values, whereas tall trees except Erman's birch showed relatively higher NDVI but lower iTVDI values. The Erman's birch iTVDI values were higher than the other tall trees. These results suggest that the difference of vegetation cover types could be one of the factors that influence iTVDI values.
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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) | 0.000 | 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".