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Record W2593202280 · doi:10.1089/can.2017.29008.crs

Proceedings from the 26th Annual Symposium of the International Cannabinoid Research Society: June 26–July 1, 2016

2017· article· en· W2593202280 on OpenAlexaboutno aff
Mauro Maccarrone

Bibliographic record

VenueCannabis and Cannabinoid Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabinoidCannabisQuarter (Canadian coin)MedicineLibrary sciencePolitical scienceNeurosciencePsychologyPsychiatryGeographyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The 26th Annual Symposium of the International Cannabinoid Research Society (ICRS) was held in the mountain town of Bukowina Tatrzanska (Krakow), Poland, on June 26–July 1, 2016. In this breathtaking location, newcomers and well-established investigators in the field of (endo)cannabinoid research presented exciting new data, with a good balance between preclinical and clinical topics, both in the central nervous system and in peripheral tissues. Potential indications for the therapeutic use of cannabis to treat various human disorders were also discussed in the conference. After more than a quarter of a century, ICRS proved to be once again a unique forum to share novel concepts on (endo)cannabinoid research at large, and most of the approximately 300 delegates voiced their support for having such a forum where diverse (and sometimes contrasting) ideas can be exchanged, fostering new collaborations.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.140
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1400.059

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.

Opus teacher head0.042
GPT teacher head0.359
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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