Bibliographic record
Abstract
This appendix is a companion to Chapter 9. In particular, it discusses two case studies that illustrate the evaluation framework laid out in that chapter and whose details were discussed all throughout the book. The first case study focuses on a practical (albeit semiartificial) domain; the second uses datasets from the UCI Repository for Machine Learning. The two studies are now discussed in turn. Illustrative Case Study 1 In this case study, we used the dataset generated by Health Canada for the 2008 ICDM Data Contest. The purpose of the data is to serve as a basis for construction of automated learning systems able to monitor the amount of a few particular xenon isotopes (radioxenon) released in the atmosphere in an effort to verify compliance of the global ban on nuclear tests (the Comprehensive Nuclear Test Ban Treaty or (CTBT). These isotopes, when released in some given pattern, are characteristic of nuclear explosions. What makes the problem difficult, however, is that the monitoring stations are typically not located at the site of the explosion. Instead, the isotopes are transported, over days or weeks, through various weather systems, toward these stations and, in the process, lose their characteristic pattern. This is further complicated by the fact that xenon isotopes in various quantities are present in the atmosphere at the sites of the monitoring stations. This is due to the release of such gases by perfectly legal civil nuclear plants such as medical isotope production facilities and nuclear power plants.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".