Bibliographic record
Abstract
Depression is a disease that concerns a growing group of people: children, youth, adult and the elderly. Statistics indicate that 5 up to 20% of the population of the globe suffer from it. Women suffer from this disease four times more often than men. Depression is such a mental state in which the patient has a deep sense of meaninglessness of life and sees the world in the gloomy colors. Such a person is depressed, their mood affects the job, interpersonal relationships, and learning process. They have a feeling of hopelessness, worthlessness, pessimism and sadness. In many cases patients suffering from depression contemplate or attempt suicide. They cannot notice the solution of their problems. Moreover, they cannot imagine their future, if they were stopped in time. For such people only the past and their past failures matter. Time stopped for them. In addition to endogenous depression, there is also autumn-winter depression, which is a seasonal disease. The first symptoms appear in early autumn. The improvement of the patients’ mental health is observed from February whereas the next symptoms of the disease appear spontaneously in March and April. Winter depression is caused by a prolonged night in autumn and winter and this fact results in the prolonged synthesis of melatonin by the pineal gland, which affects the development of depression.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".