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Record W2564834948 · doi:10.1139/cjss-2016-0011

Payback period in cranberry associated with a wireless irrigation technology

2016· article· en· W2564834948 on OpenAlex
Tiphaine Jabet, Jean Caron, Rémy Lambert

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Soil Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIrrigationDrainageEnvironmental scienceTensiometer (surface tension)Agricultural engineeringHydrology (agriculture)WirelessPayback periodWater resource managementComputer scienceAgronomyEngineeringProduction (economics)Geotechnical engineering

Abstract

fetched live from OpenAlex

A payback (PB) period calculation study was performed to evaluate the relevance of using a real-time tensiometer technology to manage irrigation in cranberries. By using wireless tensiometers collecting real-time information at different locations in the field, growers can avoid over or under irrigating some of their beds. They can also identify underlying subsurface hydrological processes that may affect crop growth. The study is based on a survey of problems encountered in the field and on the yield response associated with changes in irrigation practices in case studies and research experiments. It shows that the gains associated with the use of real-time wireless tensiometers and subsequent modifications made to drainage and irrigation in some of the beds generated a PB within a year, in most scenarios, with the price of cranberries between CAN$0.12 and $0.38 lb−1, for farm operations covering 20–400 ha (50–1000 acres).

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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