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Record W2068377421 · doi:10.1109/icnsc.2007.372898

Real-time Monitoring System for Odours around Livestock Farms

2007· article· en· W2068377421 on OpenAlex
Leilei Pan, Rui Liu, Shanghong Peng, Simon X. Yang, Stefano Gregori

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLivestockElectronic noseWireless sensor networkComputer scienceReal-time computingEnvironmental scienceComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

A sensor network-based livestock farm odour monitoring system is proposed for monitoring and analyzing livestock farm odour remotely. The system utilizes a wireless sensor network built from electronic nose nodes which can detect odour compounds and environment factors such as temperature and humidity. The architecture of the system and the functionality of each component is introduced. The proposed odour monitoring system can provide farmers and researchers with more precise odour management capabilities for more efficient operation of odour reduction systems such as ventilation fans. It can aid the development of an optimal overall odour management strategy by providing real-time, detailed data about the livestock farm environment and odour dispersion.

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 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: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.014
GPT teacher head0.245
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

Quick stats

Citations15
Published2007
Admission routes1
Has abstractyes

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