Setting Limits, Computing Intervals, and Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article discusses a number of statistical aspects of source detection, thecomputation of intervals and upper limits for a source intensity, and accessingthe sensitivity of a detection procedure. Emphasis is placed on model diag-nostics, validation, and improvement as means of avoiding odd behaviors in theseprocedures such as over abundant short or empty intervals. Improved modelspecification is viewed as a better response to systematic uncertainties, thelook elsewhere effect, and general model inadequacy than simply insisting on asignificance level of 5σ for source detection. We advocate reporting both theupper limit and the sensitivity to better represent the strength of evidence fordetection and the reported source intensity. Finally, we explore the use ofdecision theoretic analysis to derive detection procedures, intervals, andlimits in order to focus attention on the statistical properties of primaryinterest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it