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Record W2088916618 · doi:10.4296/cwrj3503339

Private Irrigators in Southern Alberta: A Survey of Their Adoption of Improved Irrigation Technologies and Management Practices

2010· article· en· W2088916618 on OpenAlexvenueaboutno aff
Lorraine A. Nicol, Henning Bjørnlund, K. K. Klein

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2010
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationSubsidyIrrigation managementBusinessProductivityLicenseAgricultural economicsQuarter (Canadian coin)HectareAgricultureFinanceEconomicsGeographyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Private irrigators account for about one-quarter of total irrigated area in southern Alberta with the balance of irrigation taking place within 13 irrigation districts. Some 1,367 private irrigation license holders exist in Alberta with a total irrigable area of more than 125,000 hectares. This group's participation in achieving the province's Water for Life objective of a 30 percent increase in water efficiency and productivity is important. Based on a random survey of private irrigators, it was found that they are grounded in family farm traditions, have been slow to adopt improved irrigation technologies and management practices in the past, and have even less intention of doing so in the future. A host of factors are causing this low adoption rate, but foremost are financial constraints and physical field conditions. Large cash subsidies or the establishment of additional processing facilities especially for Timothy hay and alfalfa would be required to motivate this group to adopt improved irrigation technologies. Adopting improved management practices may occur more readily since these would not involve large financial outlays. Water savings may be possible since the decision of when and how much to irrigate continues to be based on the long-standing tradition of eye-balling the crop's condition and judging the weather.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.188
Teacher spread0.177 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2010
Admission routes2
Has abstractyes

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