Bibliographic record
Abstract
The papers in this special section are the proceedings of a symposium on chronobiology and mood disorders presented at the June 1999 meeting of the Canadian College of Neuropsychopharmacology in Halifax, Nova Scotia. These papers review aspects of mood disorders that are linked to biological rhythms with 3 different periodicities: daily, menstrual and annual. They also reflect a growing interest in mechanisms regulating physiological rhythmicity on a variety of time scales and in the impact of normal and pathologic function of these mechanisms on psychiatric illnesses. Rhythms are related to mood disorders in several different ways. First, some conditions are expressed cyclically; that is, they have a period linked to an identified internal or external periodicity, such as menstrual and annual rhythms of dysphoria or depression. Other conditions, such as many cases of bipolar illness, express cycles that are not tied to any identified periodic stimulus. A second connection between rhythms and mood disorders is the alteration of daily rhythms, in particular sleep and waking, in mood disorders. Finally, the systems responsible for the generation or synchronization of normal rhythms may be involved in the etiology of mood disorders, as has sometimes been proposed, for example, for bipolar illness and for seasonal affective disorders. Connections between psychiatric disorders and biological rhythms were reviewed more than 40 years ago in a landmark monograph by Curt Paul Richter, whose innovative concepts and studies gave birth to many fields of psychobiological research, as well as establishing key concepts and analytic methods for the study of biological rhythms. Richter’s 1965 monograph Biological Clocks in Medicine and Psychiatry presented material from two 1959 Salmon Lectures to the New York Academy of Medicine. It serves as a benchmark for assessing the developments in this area of research over the last few decades.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".