Compilation of the MOPEX 2004 results
Bibliographic record
Abstract
As part of the MOPEX 2004 workshop, the participants were asked to submit simulations using the common database provided for the workshop (see Chahinian et al this issue). The simulations were then analysed and the evaluation criteria computed to compare the models' performance for the gauged and ungauged modes using six criteria describing the model's performance in both high and low flow conditions. The comparisons were undertaken for all three participation levels (i.e. 3, 12 and 40 catchments).\nThe results indicate that on the 3 catchment level model ranking may vary according to the tested criterion and catchment. Hence a larger number of catchments are necessary to evaluate the models' performance. Among the 10 models tested on the 12 and 40 catchment samples in gauged mode, GR5H, Mordor and SAC-SMA rank as the top three models. When analysing model results in the ungauged mode, SAC-SMA ranks as the best of the four tested models. The analysis of the submitted files highlights the need for continuing efforts to develop model parameterisation strategies for ungagued catchments in order to improve prediction in ungauged basins (PUB).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.031 |
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".