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Record W4404093547 · doi:10.1016/j.atech.2024.100634

A comprehensive cost mapping of digital technologies in greenhouses

2024· article· en· W4404093547 on OpenAlex
Carolina Vargas, Sébastien Gamache, Nilson Henao, Kodjo Agbossou, Shaival H. Nagarsheth

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSmart Agricultural Technology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGreenhouse Technology and Climate Control
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Trois-Rivières
KeywordsGreenhouseComputer scienceEnvironmental scienceAgronomyBiology

Abstract

fetched live from OpenAlex

Conventional greenhouse producers face significant challenges in integrating advanced Industry 4.0 technologies into their production processes. One of the main obstacles is the lack of clarity regarding the components of technological costs. This article develops a cost mapping of the implementation of such technologies in the context of greenhouses. The mapping distinguishes between capital expenditures-CAPEX and operational expenditures-OPEX, categorizing the key technological components and their financial implications. Based on general findings through the literature review, a number of cost areas can be identified and classified, respectively: material acquisition, installation and retrofitting, integration and customization, software services, and operation and maintenance costs. This cost structure will be a basis for future economic analyses and cost-benefit (CBA) models, promoting strategic decision-making and a more informed and precise selection of digital technologies.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.223
Teacher spread0.202 · 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