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Record W2052218988 · doi:10.3390/antibiotics2020191

Tracking Change: A Look at the Ecological Footprint of Antibiotics and Antimicrobial Resistance

2013· review· en· W2052218988 on OpenAlex

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

VenueAntibiotics · 2013
Typereview
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAntibiotic resistanceAntibioticsBiocideEcosystemContaminationHuman healthResistance (ecology)PollutantAntimicrobialPollutionBiologyEnvironmental scienceEcologyEnvironmental healthMicrobiologyMedicine

Abstract

fetched live from OpenAlex

Among the class of pollutants considered as 'emerging contaminants', antibiotic compounds including drugs used in medical therapy, biocides and disinfectants merit special consideration because their bioactivity in the environment is the result of their functional design. Antibiotics can alter the structure and function of microbial communities in the receiving environment and facilitate the development and spread of resistance in critical species of bacteria including pathogens. Methanogenesis, nitrogen transformation and sulphate reduction are among the key ecosystem processes performed by bacteria in nature that can also be affected by the impacts of environmental contamination by antibiotics. Together, the effects of the development of resistance in bacteria involved in maintaining overall ecosystem health and the development of resistance in human, animal and fish pathogens, make serious contributions to the risks associated with environmental pollution by antibiotics. In this brief review, we discuss the multiple impacts on human and ecosystem health of environmental contamination by antibiotic compounds.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.100
GPT teacher head0.335
Teacher spread0.235 · 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