Statistical reasoning in diagnostic problem-solving—The case of flow-rate measurements
Bibliographic record
Abstract
There are various methods for measuring flow rates in rivers, but all of them have practical issues and challenges. A period of exceptionally high water levels revealed substantial discrepancies between two measurement setups in the same waterway. Finding a causal explanation of the discrepancies was important, as the problem might have ramifications for other flow-rate measurement setups as well. Finding the causes of problems is called diagnostic problem-solving. We applied a branch-and-prune strategy, in which we worked with a hierarchy of hypotheses, and used statistical analysis as well as domain knowledge to rule out options. We were able to narrow down the potential explanations to one main suspect and an alternative explanation. Based on the analysis, we discuss the role of statistical techniques in diagnostic problem-solving and reasoning patterns that make the application of statistics powerful. The contribution to theory in statistics is not in the individual techniques but in their application and integration in a coherent sequence of studies – a reasoning strategy.
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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 | 0.044 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".