Correction to: Statistical modeling and analysis of trace element concentrations in forensic glass evidence
Bibliographic record
Abstract
The abcissa on certain figures in "Statistical Modeling and Analysis of Trace Element Concentrations in Forensic Glass Evidence" [Pan and Kafadar (2018)] and the corresponding supplement have been corrected.Estimated match rates are around 20-30% lower than originally stated (when the true relative difference in concentrations is less than 15% in all elements); however, the main results and conclusions of the paper remain unchanged.Two samples that come from batches whose mean log concentrations differ by δ = 0.1 (roughly 10%) in all 17 elements would not be "considered distinguishable" [ASTM International (2016), Section 11.1.7]62.22-65.41% of the time using the covariance matrix estimate from the German data, and 77.18-78.14% of the time using the estimate from the Canadian data set.Affected figures and tables below are labeled corresponding to Pan and Kafadar (2018).Section 2 contains corrected supplemental figures and tables.TABLE 3 Canadian and German data simulation match rates at various δ (δ) 0.05 0.1 0.15 0.2 0.25 0.3 (a) Canadian data match rates t 3 0.991 0.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".