Processing Of Remotely Sensed Data To Derive Useful Input Data For The Hydrotel Hydrological Model
Bibliographic record
Abstract
A new hydrological model, called HYDROTEL, has been developed recently [4,5]. Characterized by a modular structure and the ability to simulate spatially distributed processes, this model is able to make good use of remotely sensed (R.S.) data and digital terrain models. In practice, R.S. data are processed by IMATEL, a complementary software. In this paper, the emphasis is made on the transformation of R.S. data into useful informations for HYDROTEL, with actual or future algorithms integrated into our software package or available elsewhere. In particular, R.S. data are already, or may be, used for the estimation of watershed characteristics (topography, drainage network, river reaches, lake area, land-use and soil types) as well as for the estimation of meteorological variables (liquid or solid precipitations, characteristics of the snow cover and actual evapotranspiration).
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".