Bibliographic record
Abstract
acetaminophen 98 active suicidal ideation 24, 63-4, 68-70 see also suicidal ideation definition 24, 68-69 acute risks, warning signs 62-4, 89, 92-3, 115-16, 131-2, 141 adolescence 17-19, 94, 100-6, 112-14, 117-18, 131-2, 134, 142-3 see also age; younger people benign/dangerous changes 102-3 concepts 100-5, 142-3 confidentiality issues 104, 117-18, 131-2, 134 KADS 105, 142-3 SSRIs 104-5 statistics 100-1 suicide prevention strategies 112-14 suicide risk factors 16-19, 94, 100-6, 112-14, 142-3 warning signs 102-3, 113-14 affective (mood) disorders 3-4, 8, 18-19, 21-2, 29-33, 38-42, 53-5, 79-85, 89-93, 99-102, 99-105, 120-2, 141 see also bipolar . ..; depression; mental disorders concepts 31-3, 38-40, 53-4, 89-93, 120-2, 141 definition 31-3 statistics 31-2 affective symptoms, suicide risk factors 38-41, 53-5, 64, 75-8, 79-85, 89-93, 116, 119-21, 141 age 3, 13-14, 17-19, 22-3, 27-8, 35-6, 41, 51, 79, 87, 90-1, 94, 100-8, 112-14, 141, 142-3 see also adolescence; elderly people; middle . ..; younger people developing countries 22-3 self-harm (self-injurous behaviour) 27 statistics 3, 17-18, 22-3, 100-1 underestimated prevalence/burden of suicide 17-18 age-adjusted suicide rates 2, 3, 17-18 aggression 16, 38-39, 47, 53, 91, 100-6, 141 agitation 39-40 akathasia 35-6, 38, 39, 53 see also restlessness Suicide Risk Management: A Manual for Health Professionals, Second Edition.Sonia Chehil and Stan Kutcher.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.748 | 0.632 |
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".