SmoCuDa: A Validated Smoking Cue Database to Reliably Induce Craving in Tobacco Use Disorder
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Cue-reactivity paradigms provide valuable insights into the underlying mechanisms of nicotine craving in nicotine-dependent subjects. In order to study cue-driven nicotine craving, robust and validated stimulus datasets are essential. OBJECTIVES: The aim of this study was to generate and validate a large set of individually rated smoking-related cues that allow for assessment of different stimulus intensities along the dimensions craving, valence, and arousal. METHODS: The image database consisted of 330 visual cues. Two hundred fifty smoking-associated pictures (Creative Commons license) were chosen from online databases and showed a widespread variety of smoking-associated content. Eighty pictures from previously published databases were included for cross-validation. Forty volunteers with tobacco use disorder rated "urge-to-smoke," "valence," and "arousal" for all images on a 100-point visual analogue scale. Pictures were also labelled according to 18 categories such as lit/unlit cigarettes in mouth, cigarette end, and cigarette in ashtray. RESULTS: Ratings (mean ± SD) were as follows: urge to smoke, 44.9 ± 13.2; valence, 51.2 ± 7.6; and arousal, 54.6 ± 7.1. All ratings, particularly "urge to smoke," were widely distributed along the whole scale spectrum. CONCLUSIONS: We present a novel image library of well-described smoking-related cues, which were rated on a continuous scale along the dimensions craving, valence, and arousal that accounts for inter-individual differences. The rating software, image database, and their ratings are publicly available at https://smocuda.github.io.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it