Corrigendum to “Threatening faces induce fear circuitry hypersynchrony in soldiers with post-traumatic stress disorder”
Bibliographic record
Abstract
In the original published version of this article, Fig. 1A included facial images of individuals who had not provided consent for their use in this publication. The authors apologize for this oversight. In the updated Fig. 1A these images were replaced with images of individuals whose consent has been obtained. The Materials and Methods section was updated to include the following acknowledgement: ‘Development of the MacBrain Face Stimulus Set was overseen by Nim Tottenham and supported by the John D. and Catherine T. MacArthur Foundation Research Network on Early Experience and Brain Development. Please contact Nim Tottenham at [email protected] for more information concerning the stimulus set.’ as well as a reference in which these images were previously published (Tottenham et al., 2009). The reference list was updated accordingly. These corrections do not, in any way, compromise the findings of the study, either in terms of the methodology, results, or interpretations drawn from the data therein. Both the HTML and PDF versions of the article have been updated to correct the error. Threatening faces induce fear circuitry hypersynchrony in soldiers with post-traumatic stress disorderDunkley et al.HeliyonJanuary 20, 2016In BriefPost-traumatic stress disorder (PTSD) is associated with atypical responses to emotional face stimuli with preferential processing given to threat-related facial expressions via hyperactive amygdalae disengaged from medial prefrontal modulation. Full-Text PDF Open Access
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.327 | 0.099 |
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".