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Record W2331599801 · doi:10.1142/s2382624x16710016

Policy Note: Reversing Salt-Induced Land Degradation Requires Integrated Measures

2016· article· en· W2331599801 on OpenAlexaff
Manzoor Qadir

Bibliographic record

VenueWater Economics and Policy · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsLand degradationAridIrrigationReversingEnvironmental scienceSustainabilityWater resource managementAgricultureNatural resource economicsFood securityBusinessEnvironmental planningGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

Agricultural crops take up water, but not salt, and evaporation from irrigated land does likewise. The result is increasing salt levels in soils. Just as cities cannot ignore urban wastewater collection and treatment, irrigating farmers and irrigation districts cannot ignore what to do with the salt in agricultural drainage water. Although salt management techniques can seem straightforward, the long-term sustainability of irrigation in arid and semi-arid areas, where most irrigation takes place remains a challenge. Salt-induced land degradation is on the rise in several major river basins. Salt-affected lands remain valuable resources that cannot be easily abandoned, given their importance for food security and regional economies, as well as the significant investments in infrastructure that have been made on these lands. This policy note discusses the status of salt-induced land degradation and addresses two key questions: Why has progress been so limited in addressing salt-induced land degradation? And what measures could be taken to prevent and reverse such degradation?

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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 designBench or experimental
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

Citations9
Published2016
Admission routes1
Has abstractyes

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