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Record W1964477632 · doi:10.4103/1463-1741.153373

ICBEN review of research on the biological effects of noise 2011-2014

2015· review· en· W1964477632 on OpenAlexaff
Mathias Basner, Mark Brink, A Bristow, Yvonne de Kluizenaar, Lawrence S. Finegold, Ji‐Young Hong, Sabine A. Janssen, Ronny Klæboe, Tony Leroux, Andreas Liebl, Toshihito Matsui, Dieter Schwela, Mariola Śliwińska‐Kowalska, Patrik Sörqvist

Bibliographic record

VenueNoise and Health · 2015
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNoise (video)MandateNoise exposurePsychologyHuman healthMedicineHearing lossAudiologyComputer scienceEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

The mandate of the International Commission on Biological Effects of Noise (ICBEN) is to promote a high level of scientific research concerning all aspects of noise-induced effects on human beings and animals. In this review, ICBEN team chairs and co-chairs summarize relevant findings, publications, developments, and policies related to the biological effects of noise, with a focus on the period 2011-2014 and for the following topics: Noise-induced hearing loss; nonauditory effects of noise; effects of noise on performance and behavior; effects of noise on sleep; community response to noise; and interactions with other agents and contextual factors. Occupational settings and transport have been identified as the most prominent sources of noise that affect health. These reviews demonstrate that noise is a prevalent and often underestimated threat for both auditory and nonauditory health and that strategies for the prevention of noise and its associated negative health consequences are needed to promote public health.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.007
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.516
GPT teacher head0.617
Teacher spread0.102 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations136
Published2015
Admission routes1
Has abstractyes

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