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Record W1508535529 · doi:10.1002/9783527610211.ch2

Environmental Studies: Sources and Pathways

2006· other· en· W1508535529 on OpenAlexaboutno aff
Kai Bester

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicEnvironmental Chemistry and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTriclosanEnvironmental chemistrySewage sludgeSewagePlasticizerChemistryEnvironmental impact of pharmaceuticals and personal care productsOrganophosphateSewage treatmentFire retardantEnvironmental scienceOrganic chemistryPesticideEnvironmental engineeringEcologyBiology

Abstract

fetched live from OpenAlex

This chapter contains sections titled: Synthetic Fragrance Compounds in the Environment Polycyclic Musk Fragrances in Sewage Treatment Plants Experimental Background Mass Balance Assessment Multi-step Process Study on Polycyclic Musks Polycyclic Musk Fragrances in Diverse Sludge Samples Polycyclic Musk Fragrances in Surface Waters Experimental Methods Results and Discussion Polycyclic Musk Fragrances in the North Sea OTNE and Other Fragrances in the Environment Methods Results and Discussion Other Fragrances: Nitroaromatic Musks and Macrocyclic Musks Behavior of Polycyclic and Other Musk Fragrances in the Environment The Bactericide Triclosan and Its Transformation Product Methyl Triclosan in the Aquatic Environment Bactericides from Personal Care Products in Sewage Treatment Plants Materials and Methods Triclosan Balances in a Sewage Treatment Plant Triclosan in Multi-step Processes in Sewage Treatment Plants Triclosan in Sewage Sludge Triclosan in Surface Waters Estimation of Elimination Constants for Triclosan in a River Discussion on Triclosan and Methyl Triclosan in the Environment UV Filters/Sunscreens Endocrine Properties of UV Filters UV Filters in Aquatic Ecosystems Enantioselective Considerations for UV Filters Organophosphate Flame-retardants and Plasticizers Introduction Flame-retardants Organophosphate Plasticizers The Organophosphate Flame-retardant TCPP in a Sewage Treatment Plant Materials and Methods Mass Balance Assessment for TCPP in a Sewage Treatment Plant TCPP in Sludge Monitoring Evaluation of the TCPP Data Organophosphate Flame-retardants and Plasticizers in Multi-step Sewage Treatment Materials and Methods Results and Discussion Conclusions Organophosphorus Flame-retardants and Plasticizers in Surface Waters Materials and Methods Results and Discussion Organophosphates in Drinking Water Purification Materials and Methods Results Conclusions Organophosphates and Other Compounds in the North Sea and Lake Ontario: A Comparison Materials and Methods Results and Discussion Conclusions Overall Discussion on Chlorinated Organophosphorus Flame-retardants and Other Compounds Endocrine-disrupting Agents Introduction to Endocrine-disrupting Effects Estrogenic Hormones and Antibiotics in Wastewater Treatment Plants Description of the Sample Sites Results and Discussion Conclusions Nonylphenol and Other Compounds in the North Sea Materials and Methods Results Discussion Conclusions Diverse Compounds Benzothiazoles in Marine Ecosystems Materials and Methods Results Discussion and Conclusions Enantioselective Degradation of Bromocyclene in Sewage Treatment Plants Introduction Methods and Materials Results and Discussion

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.029
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0290.008

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 head0.007
GPT teacher head0.187
Teacher spread0.179 · 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".

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Citations0
Published2006
Admission routes1
Has abstractyes

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