Albemarle reports third quarter results
Bibliographic record
Abstract
Research on smoking cessation has found consistencies and similarities during abstinence, but also that the specific signs and symptoms and their intensity vary greatly from individual to individual. One possible source of this variation is the cognitions associated with quitting. We investigated the experiences and associated cognitions in normal cessation by asking quitting smokers to rate their experiences on a questionnaire and to indicate the most likely reason for each experience. Statistical analyses confirmed that attributions to abstinence were significantly higher for increased negative experiences, and there were significantly more reattributions than would be found by chance for items associated with smoking abstinence. Significantly more attributions to abstinence were made by clinic attendees and significantly more attributions of negative experiences to abstinence were made by unaided quitters using self-help materials. These results can be interpreted in the context of attribution theory; quitters may use the cognitions available to them to attribute their negative experiences to quitting. Consequently, counsellors could use cognitive therapy to alter their clients' expectations and explanations of their experiences, and emphasise the positive outcomes of cessation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.361 | 0.164 |
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".