Correction to “Chemical Composition of Commercial Cannabis”
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ARTICLES ASAPPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to "Chemical Composition of Commercial Cannabis"David S. Wishart*David S. Wishart*[email protected]More by David S. Wishart, Mickel Hiebert-GiesbrechtMickel Hiebert-GiesbrechtMore by Mickel Hiebert-Giesbrecht, Gozal InchehborouniGozal InchehborouniMore by Gozal Inchehborouni, Xuan CaoXuan CaoMore by Xuan Cao, An Chi GuoAn Chi GuoMore by An Chi Guo, Marcia A. LeVatteMarcia A. LeVatteMore by Marcia A. LeVatte, Claudia Torres-CalzadaClaudia Torres-CalzadaMore by Claudia Torres-Calzada, Vasuk GautamVasuk GautamMore by Vasuk Gautam, Mathew JohnsonMathew JohnsonMore by Mathew Johnson, Jaanus LiigandJaanus LiigandMore by Jaanus Liigand, Fei WangFei WangMore by Fei Wanghttps://orcid.org/0000-0002-0191-9719, Shirin ZahraeiShirin ZahraeiMore by Shirin Zahraei, Sudarshana BhumireddySudarshana BhumireddyMore by Sudarshana Bhumireddy, Yilin WangYilin WangMore by Yilin Wang, Jiamin ZhengJiamin ZhengMore by Jiamin Zheng, Rupasri MandalRupasri MandalMore by Rupasri Mandal, and Jason R. B. DyckJason R. B. DyckMore by Jason R. B. DyckCite this: J. Agric. Food Chem. 2024, XXXX, XXX, XXX-XXXPublication Date (Web):February 27, 2024Publication History Received15 February 2024Published online27 February 2024https://doi.org/10.1021/acs.jafc.4c01418© 2024 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0. License Summary*You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is Open Access under the license indicated. Learn MoreArticle Views-Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (686 KB) Get e-Alertsclose Get e-Alerts
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.218 | 0.126 |
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".