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Record W2063934260 · doi:10.5539/jsd.v7n2p159

Climate Variability and Crop Production in Uganda

2014· article· en· W2063934260 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Sustainable Development · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsCropEnvironmental scienceClimate changeIrrigationCrop productionProduction (economics)GeographyAgricultureAgronomyEcologyEconomicsBiologyForestry

Abstract

fetched live from OpenAlex

In this paper, the relationship between climate variation and crop output in Uganda for the period 1981 to 2008 is examined. The time-varying ARCH model of the crop production function is used to estimate the relationships. Analysis of the incidence of rainfall and temperature variation from the long-term average indicates that it is insignificant. Estimates of the trend of rainfall and temperature suggest a gradual decline in volume of rainfall and record of temperatures in Uganda in the present and near future. ARCH model estimates show that a variation in rainfall and temperature from the long-term mean has significant effects on crop output, while exponential increase in rainfall has detrimental effect on crop output. It is recommended that the government should support farmers to adopt small-scale irrigation systems; and capacity of weather forecast agencies should be strengthened to monitor and educate the public on present and potential near-future climate variations.

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)0.002
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 score0.511
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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