A user's approach to assess numerical codes for saturated and unsaturated seepage conditions
Bibliographic record
Abstract
Numerical models are useful tools to evaluate problems and design remedial measures relative to groundwater seepage. They provide information for decision-making and guidance for collecting new data. Most users do not know in detail how the numerical code works. However, they must be sure that it gives reliable predictions for the problem under study. The major questions relative to the computer calculations are as follows: Can the results of the code be trusted? Under which conditions and to what extent are its predictions uncertain? This paper describes an approach that can be followed by model users to evaluate the results of a groundwater numerical code. This approach is relatively general, although each code is unique and may require specific controls. It begins with simple problems and progressively moves towards more complex problems: from steady-state to unsteady-state conditions, from one- to three-dimensional problems, and from saturated to saturatedunsaturated conditions. This approach is illustrated with a commercial code that passed the successive tests.Key words: groundwater, numerical code, quality control, saturated and unsaturated seepage.
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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 | 0.040 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".