Construction probabiliste de scénarios d'apports à un réservoir
Bibliographic record
Abstract
The behaviour of a hydric system depends on three factors : (i) the state of the installation, (ii) the operating rules, and (iii) the inflows. While the first two factors are (in theory) known to the manager, the third can only be estimated by means of more or less precise forecasts. A significant part of the risk, to which is subjected the system at a given time, is induced by the uncertainty in the future inflows. The evaluation of this uncertainty is therefore a first step in the incorporation of risk into management. Its evaluation is then a stage preliminary to the integration of the risk in management. A method of construction of inflow scenarios starting from an arbitrary date t of the year is developed in this paper. It uses a Markovian process formerly developed by the authors to model short-term uncertainty in stream flow. These scenarios, which are not equiprobable, are built to reproduce the statistical behaviour of the river or reservoir and have the shape of an event tree whose structure is defined by the user before application of the method. Two examples of application on two rivers located in Quebec, Canada, are presented.Key words: reservoir operation, previsions, inflows, risk, uncertainty.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".