Characterizing the Effects of Kinloch Wellness Ltd. CBZ™
Bibliographic record
Abstract
This study assessed the sleep and sleep-related effects of a tincture product containing CBD, CBN, and terpenes: Kinloch Wellness Ltd. CBZ™ - CBN + CBD 900 Apple Cinnamon H2O-Rapid™ Tincture. A multi-modal, dual-component study design was conducted remotely and locally at Zentrela’s laboratory in Hamilton, Ontario. Objective neurophysiological data were collected using Zentrela’s EEG-based Cognalyzer® AI platform, complemented by validated subjective measures including the Drug Effects, Questionnaire (DEQ) and BRUMS test. The Cognalyzer® (a novel artificial intelligence platform developed by Zentrela Inc.) was applied to objectively quantify product effects through EEG signals obtained via a proprietary headset worn during laboratory visits. Across the study cohort (n = 15), results indicate that the investigational product induces acute mood-enhancements, potentially conducive to relaxation and sleep onset. Biometric sleep-tracking data further indicated that investigational product increased the duration of deep sleep (non-REM stage N3). Using EEG, subjective self-reporting, and wearable-based biometric measures, the effects of Kinloch CBZTM are characterized as acutely mood-enhancing and, in the short term, rest-enhancing
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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) | 0.004 | 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".