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 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

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.

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)0.001
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 score0.739
Threshold uncertainty score0.126

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.235
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