MétaCan
Menu
Back to cohort
Record W4416571100 · doi:10.1080/00220388.2025.2581604

Ponds and Their Potential for Agricultural Sustainability in Punjab; Insights from a Century of Surface Water Change in the Granary of India

2025· article· en· W4416571100 on OpenAlexaff
Adam S. Green, Aftab Alam, Shruti Bhogal, Sandeep Dixit, Kamal Vatta, Cameron A. Petrie

Bibliographic record

VenueThe Journal of Development Studies · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFisheries and Aquaculture Studies
Canadian institutionsArthur B. McDonald-Canadian Astroparticle Physics Research Institute
FundersBanaras Hindu UniversityBiotechnology and Biological Sciences Research CouncilDirectorate for Biological SciencesGlobal Challenges Research Fund
KeywordsAgricultureSustainabilityClimate changeFarm waterSurface waterWater resourcesGranary

Abstract

fetched live from OpenAlex

Ponds are one of the most basic landscape features that humans can use to manage water, and were important landscape features common especially in periods before people began extracting groundwater using fossil-fuels. In this article we use historical cartography and geospatial methods to analyse a century of change in pond distribution in the Indian state of Punjab, part of a larger area colloquially known as the ‘Granary’ of India. We ask how the changing spatial distribution of ponds over the last century reflects shifts in water management over a period that also includes the Green Revolution (1968 onwards), when agriculture intensified and groundwater levels declined. We find that ponds were prevalent in the past, suggesting they contributed to more adaptive forms of water use. Pond numbers and area have declined over the last century, which leads us to suggest that pond restoration may provide a pathway to sustainable water governance in the region today.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.004
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.235
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Development StudiesSame topicFisheries and Aquaculture StudiesFrench-language works237,207