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Calculating the cost of irrigation induced soil salinization in the tungabhadra project

2004· article· en· W2155188666 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.

Bibliographic record

VenueAgricultural Economics · 2004
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsAcadia University
FundersShastri Indo-Canadian Institute
KeywordsSoil salinityIrrigationEnvironmental scienceSoil retrogression and degradationProduction (economics)Natural resource economicsRange (aeronautics)Water resource managementSoil waterDistribution (mathematics)EconomicsAgricultural engineeringSoil scienceMathematicsMicroeconomicsEcologyEngineering

Abstract

fetched live from OpenAlex

Abstract Irrigation projects in developing countries have a history of poor performance. Inefficiencies result as water applications deviate from plans and induce greater than projected rates of soil degradation through water logging and salt accumulation. Over time, the collective impact of these forces will converge to an equilibrium with a level of output that may be far below the system's potential. The Tungabhadra Project in south west India is experiencing all of these problems. Integrating geographic, hydrologic, biologic and economic features, the lost production value is estimated for a range of equilibria to which this system may converge. For the lower left bank main canal of the Tungabhadra project, the total economic cost of soil degradation are approximately 14.5% of the system's productive potential while sub‐optimal distribution losses may approach 37.1%

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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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.154

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 head0.017
GPT teacher head0.196
Teacher spread0.179 · 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