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
Drought is a kind of natural disaster occurring frequently and affecting agricultural production in China,and it is important to improve the ecological environment,optimize the land allocation and water resources redistribution,and relieve the shortage of ecological resources in arid zone. The MODIS sensor is significantly advantageous in monitoring land use change because of its high spectral resolution,high temporal resolution and appropriate spatial resolution. Temperature vegetation drought index( TVDI) is widely used,and there are many evidences to reveal that it is a reasonable and effective way in monitoring land use change related significantly to drought.TVDI is of an important theoretical significance in drought monitoring,crop irrigation,agricultural production,pasture conservation,forest fire detection,etc. By using TVDI method from MODIS product,the spatiotemporal distribution of drought can be derived to explore the relationship between different land use types and drought over the Songliao Plain based on eco-geographical regionalization. The eco-geographical regionalization system is based on the biological and non-biological factors. This study revealed that TVDI turns out to be an effective way to get drought conditions,and the result was consistent with Zheng Du's eco-geographical regionalization theory. Temporal and spatial variation of drought in the study area was quite different from different time and different subregions during the period from 2002 to 2009. The area of drought was the largest in 2009 but the smallest in 2004. Holistically,the study area was in a wetting trend,and the proportion of wetting area including mainly the cropland,woodland and grassland was as high as 84. 95%. TVDI could not be used to monitor waters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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