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Record W2158974279 · doi:10.1109/icsens.2004.1426227

Unwired wine: sensor networks in vineyards

2006· article· en· W2158974279 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsWireless sensor networkVineyardSoftware deploymentWineComputer scienceAgricultureAgricultural engineeringInvestment (military)Environmental scienceReal-time computingEngineeringGeographyComputer networkSoftware engineering

Abstract

fetched live from OpenAlex

This paper describes the design, deployment, and output of a large-scale wireless sensor network in agriculture. We began with ethnographic research to determine needs. This research informed the design of a network that was then deployed in a working vineyard. Finally, the sensor data were analyzed for agricultural significance. Our dense-monitoring of temperature allowed us to track, among other things, not only fruit maturity as it responded to heat accumulation and but also damage from freezing as the plants entered dormancy. This paper shows that dense on-the-ground monitoring can have a substantial impact on agricultural practices, quality, yield, and, most significantly, the value of the crop. More than that, this paper demonstrates how a working deployment that provides return on investment (ROI) for the sensor network owner requires domain knowledge about the phenomena to be monitored.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.402

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 head0.003
GPT teacher head0.170
Teacher spread0.168 · 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

Citations77
Published2006
Admission routes1
Has abstractyes

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