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Record W4413257561 · doi:10.1126/sciadv.adu6611

Biocultural vulnerability of traditional crops in the Indian Trans-Himalaya

2025· article· en· W4413257561 on OpenAlexaff
Harman Jaggi, Katherine A. Solari, Alejandra Echeverri, Rinchen Tobge, Tanzin Tsewang, Kulbhushansingh Suryawanshi, Shripad Tuljapurkar

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsFuture EarthCentre for Global Health Research
Fundersnot available
KeywordsAgricultural biodiversityAgroforestryAgricultureGenetic erosionFood securityGeographyVulnerability (computing)Crop diversityEthnobiologyCash cropBiodiversityBiologyCropTraditional knowledgeEcologyGenetic diversitySociology

Abstract

fetched live from OpenAlex

Traditional agricultural landscapes are vital reservoirs of biocultural heritage and agrobiodiversity, yet traditional farming systems and their unique crop landraces face increasing marginalization and genetic erosion. Using northwest Himalaya as a case study, we examine the ecological resilience and genetic diversity of an understudied traditional crop, black pea (scientific name unclear), alongside barley ( Hordeum vulgare ), and compare them to the introduced cash crop, green pea ( Pisum sativum L. ). Participatory field experiments with local farmers revealed that traditional crops outperform introduced varieties in survival and reproduction traits across sites. To our knowledge, we generate the first whole-genome sequencing data for black peas. Clustering and nutritional analyses highlight black pea’s genetic richness and dietary potential. Our findings underscore the importance of integrating traditional ecological knowledge with ecological science to sustain agrobiodiversity, enhance climate resilience, and promote sustainable food systems. We provide insights for global agrifood innovations and socioecological stability in fragile mountain ecosystems.

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 score0.580
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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