Appendix 3: 6‐Item Kutcher Adolescent Depression Scale (KADS)
Bibliographic record
Abstract
1. Low mood, sadness, feeling blah or down, depressed, just can't be bothered.0 -Hardly ever 1 -Much of the time 2 -Most of the time 3 -All of the time 2. Feelings of worthlessness, hopelessness, letting people down, not being a good person.0 -Hardly ever 1 -Much of the time 2 -Most of the time 3 -All of the time 3. Feeling tired, feeling fatigued, low in energy, hard to get motivated, have to push to get things done, want to rest or lie down a lot.0 -Hardly ever 1 -Much of the time 2 -Most of the time 3 -All of the time 4. Feeling that life is not very much fun, not feeling good when usually (before getting sick) would feel good, not getting as much pleasure from fun things as usual (before getting sick).0 -Hardly ever 1 -Much of the time 2 -Most of the time 3 -All of the time 5. Feeling worried, nervous, panicky, tense, keyed up, anxious.0 -Hardly ever 1 -Much of the time 2 -Most of the time 3 -All of the time 6.Thoughts, plans or actions about suicide or self-harm.0 -Hardly ever 1 -Much of the time 2 -Most of the time 3 -All of the time
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.014 |
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".