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Record W2180673252 · doi:10.1080/02508060.2015.1086257

Changing to more efficient irrigation technologies in southern Alberta (Canada): an empirical analysis

2015· article· en· W2180673252 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWater International · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsIrrigationUpgradeAgricultural economicsFlood mythBusinessEmerging technologiesEnvironmental scienceQuality (philosophy)Water resource managementEconomicsGeographyComputer science

Abstract

fetched live from OpenAlex

Results from an irrigator survey in southern Alberta (Canada) indicate that more than half of irrigators changed irrigation technologies during the five-year period (crop years 2007/08–2011/12) and this potentially improved application efficiency. Changes were made from flood irrigation to wheel-move sprinklers to high- and then low-pressure pivot systems. The intended future rate of change is lower than that experienced over the previous five years. Important factors causing these changes were identified: reducing irrigation application, labour and energy inputs, and increasing crop yields and quality. Econometric modelling shows that irrigators who have commenced the process of adopting more efficient sprinklers are full-time farmers, operate their farm as corporations or partnerships, obtain information from extension agencies, and are more likely to upgrade their technologies in future.

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 score0.334
Threshold uncertainty score0.871

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 head0.030
GPT teacher head0.270
Teacher spread0.240 · 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