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Validation of a method to determine transformation of chemicals in anaerobic liquid pig and cattle manure for the OECD test guideline programme

2022· article· en· W4308541182 on OpenAlexaff
Silvia Berkner, Julia Margaretha Anke, Rolf‐Alexander Düring, Silke Fiebig, Thomas Junker, Dieter Hennecke, Monika Herrchen, Maria Meinerling, Jörg Römbke, Sören Thiele‐Bruhn, Edward Topp, Wolfgang Völkel, Susanne Walter-Rohde

Bibliographic record

VenueChemosphere · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsAgriculture and Agri-Food CanadaWestern University
Fundersnot available
KeywordsManureBiocideAgricultureGuidelineBiotechnologyEnvironmental scienceFertilizerLiquid manureWaste managementEnvironmental planningBiochemical engineeringBusinessEnvironmental protectionAgricultural engineeringPulp and paper industryEngineeringBiologyChemistryAgronomyEcologyPolitical science

Abstract

fetched live from OpenAlex

Manure is widely used as a fertilizer and applied to agricultural land. It may contain highly active chemicals like veterinary medicinal products or biocides, which enter into the environment by this pathway. This is recognized by several regulatory frameworks, however, a detailed method for examining the transformation of chemicals in manure was lacking. This article describes the validation of a method for studying the anaerobic transformation of chemicals in pig and cattle liquid manure. Different steps are covered with an emphasis on the validation ring test and the OECD (Organisation for Economic Cooperation and Development) process that led to the recent adoption of the method as OECD Test Guideline (TG) 320.

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)0.009
metaresearch head score (Gemma)0.006
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 score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.019
GPT teacher head0.271
Teacher spread0.251 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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