Bibliographic record
Abstract
In this thesis, I examined the inhibitory control of REM sleep motor activity using both a pharmacological rat model and a genetic mouse model. I characterized the role for GABA and glycine in mediating the REM-specific suppression of muscle activity as well as their involvement in regulating the phasic muscle twitches that punctuate this atonia. Based on four specific research objectives, the following conclusions were drawn:\n\n1. REM atonia is not directly mediated by glycinergic or GABAA-mediated inhibition. These data refute the prevailing hypothesis that REM atonia is caused by glycinergic inhibition. These receptors are, however, important in the regulation of phasic muscle twitch activity.\n\n2. GABAB receptors can modulate REM atonia but only when acting in concert with GABAA and glycine receptors. Blockade of all three receptor types results in a partial reversal of REM atonia, suggesting a functional interaction is occurring between these receptors during REM sleep.\n\n3. The phasic glycinergic/GABAA-mediated inhibitory drive present in REM sleep regulates the temporal pattern of phasic twitch activity that is seen across this state. I hypothesize that this progressively decreasing inhibitory input counteracts a gradually increasing excitatory input to shape the temporal distribution of muscle twitches across REM sleep. \n\n4. A loss of normal inhibitory function may play a causal role in the pathology of REM sleep behaviour disorder (RBD), the sleep disorder characterized by excessive motor activity in REM sleep.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".