Corrigendum to ‘Is there a discrete negative symptom dimension in people who use methamphetamine?’Comprehensive Psychiatry 93 (2019) 27–32
Bibliographic record
Abstract
The authors regret to inform the journal audience that three errors were published in the original version of this article. First, the total sample size of participants included in this study was 153. The sample was incorrectly listed as 154 in the Abstract (Method paragraph) and Results (Section 3.1). Second, there were two instances where the reported percentage of participants within “low-symptoms class” (Class 3) was incorrect (38%). This error occurred in the Abstract (results paragraph) and the Results (Section 3.4). In actuality, 25% of the sample (38 participants) were represented in the “low-symptoms class”. The authors would like to apologise for any inconvenience caused. Finally, when describing the exploratory factor analysis (Section 3.2), the second sentence “The three-factor model reported the lowest BIC value and the two-factor solution reported the lowest AIC value” should have read “The three-factor model reported the lowest BIC value and lowest AIC value”.
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.
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 | 0.004 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.097 | 0.073 |
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".