Subjectivity in evaluation of forecasts for intermittent streamflow - an information-theoretical perpective
Bibliographic record
Abstract
Information-theoretical evaluation of probabilistic hydrological forecasts has several advantages. Firstly, forecasts in terms of probability put the onus for correct expression of uncertainty on the forecaster, as opposed to the recipient of the forecast. Secondly, formulating the evaluation of forecast quality in terms of information-measures enables consistency with the principle of minimum description length. When applying the information-theoretical evaluation framework to forecasts of mixed-type variables, such as streamflow in rivers with intermittent flow regimes, subjectivity is introduced through the choice of units in which streamflow is measured. This can lead to preference reversals between forecasts when using certain information measures. At the hand of some examples, we explore the origins of this subjectivity, possible interpretations, as well as avenues for its resolution. Among others, the role of observation uncertainty and the physical meaning of zero flow are discussed.
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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 | 0.055 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".