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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 OpenAlex
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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

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