The Index of Cannabis Equivalence (ICE): A User-Centered Approach to Standardization of Cannabis Dose–Response
Bibliographic record
Abstract
The increasing acceptance of cannabis use, and policy changes in several jurisdictions has led researchers and public health experts to call for a standard cannabis dose. Standard dosing units are useful tools for regulation, substance use guidelines, data collection, consistency of research, as a means of communicating low-risk recommendations and dose-related effects, and for self-monitoring. Efforts to standardize cannabis dose have focused on cannabinoid content without considering tolerance or mode. Cannabis users with diverse motivations for use and varying experience rated low, medium, and high doses across seven modes of use. The participants (N = 1368; 42% female) were 18–93 years of age (M = 31.64, SD = 14.70) who had a cannabis use history. The Index of Cannabis Equivalence (ICE) identified the following low-dose cannabis equivalencies: two puffs on a joint, pipe, herbal or concentrate vaporizer is equivalent to one hit on a bong, a 5 mg/THC edible, and ¼ dab of a concentrate. These findings are based on responses from users with lower tolerance, which may limit generalizability to those with higher tolerance. The ICE proposes standardized cannabis doses through user-derived ratings across different administration routes. The meaningful standardization of units of cannabis products in a manner similar to what has been achieved for alcohol represents a valuable step in establishing standard doses across different modes of cannabis administration.
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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 | 0.135 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".