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Record W1524104636

Comparative Economics Of Users and Nonusers Of Dharabi Dam Pakistan

2009· article· en· W1524104636 on OpenAlexaff
Muhammad Aamir Khan, Bilal Mansoor, Aftab Hanif

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsMcGill University
Fundersnot available
KeywordsProductivityCroppingAgricultureFood securityIrrigationAgricultural economicsDistribution (mathematics)Agricultural productivityPovertyMarginal productAridBusinessEconomicsWater resource managementGeographyProduction (economics)Environmental scienceEconomic growthMathematicsAgronomy
DOInot available

Abstract

fetched live from OpenAlex

Water is a limiting factor for sustainable agriculture in Barani(Arid). However, rainfall is the only source of water the spatial and temporal variation of which is very high. Therefore conservation and management of this source is vital for agriculture development and socio-economic uplift of the area. This study was, mainly, also devised to address land distribution problems and consequent farm productivity in the study area. The farmers were divided into two main categories irrigated and rainfed farmers. The performance of most of the indicators i.e. yield, gross margins, farm income, labour productivity, income distribution, cropping intensity and crop diversity was found better in irrigated as compared to rainfed. While marginal factor productivity, irrigation productivity and rate of institutional credit availability was higher in irrigated area. However, rainfed area was always least efficient with respect to all of the quantified indicators. The findings of the research are helpful for the farmers of the study area in decision making among different farm enterprises. Hence it can alleviate poverty and help to bring food security in the deprived regions.

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)0.000
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 score0.787
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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