Reservoir Time-Series Filling From Remote Sensing Data in the Central Valley, Chile
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
Reservoirs play a fundamental role in the hydrological planning of the central valley of Chile as they provide water for human and animal consumption, energy generation, and crop irrigation, especially during the summer season. In agriculture, reservoirs represent a significant source to keep the food security standard for more than half of the population of the country. The water management plans need complete records of their volume to calculate rules of operation or future scenarios; however, currently, these time series include gaps that do not allow better analysis, which increases uncertainty. To address this, the authors test a methodology to assess Sentinel 2 imagery through normalized difference water index (NDWI). The results correctly represent the temporality and seasonality of reservoir dynamics; however, the magnitude of the changes is not well represented when the reservoir is delivering water. This research allows more data-based planning of water resources in the central zone, contributing to better decision-making and more efficient water management.
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 | 0.004 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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