MétaCan
Menu
Back to cohort
Record W4391492908 · doi:10.2139/ssrn.4681320

Workflow to Facilitate the Detection of New Psychoactive Substances and Drugs of Abuse in Influent Urban Wastewater

2024· preprint· en· W4391492908 on OpenAlexaff
Richard Bade, Denice van Herwerden, Nikolaos I. Rousis, Sangeet Adhikari, Darren Allen, Christine Baduel, Lubertus Bijlsma, Tim Boogaerts, Daniel A. Burgard, Andrew Chappell, Erin M. Driver, Fernando F. Sodré, Despo Fatta‐Kassinos, Emma Gracia Lor, Elisa Gracia-Marín, Rolf U. Halden, Ester Heath, Emma L. Jaunay, Alex J. Krotulski, Foon Yin Lai, Arndís Sue Ching Löve, Jake O’Brien, Jeong‐Eun Oh, Daniel Pasin, Marco Pineda, Magda Psichoudaki, Noelia Salgueiro‐González, Cezar Silvino Gomes, Bikram Subedi, Kevin V. Thomas, Νikolaos S. Τhomaidis, Degao Wang, Viviane Yargeau, Saer Samanipour, Jochen F. Mueller

Bibliographic record

VenueSSRN Electronic Journal · 2024
Typepreprint
Languageen
FieldChemistry
TopicDye analysis and toxicity
Canadian institutionsMcGill UniversityOffice of the Chief Medical Examiner
Fundersnot available
KeywordsDrugs of abuseWastewaterPsychoactive substanceWorkflowBusinessEnvironmental scienceDrugPharmacologyEnvironmental engineeringMedicineComputer sciencePsychiatry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.016

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 head0.013
GPT teacher head0.242
Teacher spread0.230 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractno

Explore more

Same venueSSRN Electronic JournalSame topicDye analysis and toxicityFrench-language works237,207