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Record W2182344941 · doi:10.3126/aej.v14i0.19796

Unlocking Uncultivated Food for Mountain Livelihood: Case from Hindu Kush Himalayas

2013· article· en· W2182344941 on OpenAlexfundno aff
Kamal Aryal, Rajan Kotru, Karma Phuntsho

Bibliographic record

VenueJournal of Agriculture and Environment · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEthnobotanical and Medicinal Plants Studies
Canadian institutionsnot available
FundersInternational Development Research CentreInternational Centre for Integrated Mountain Development
KeywordsLivelihoodFood securitySustenanceAgriculturePovertyGeographyEcosystem servicesBusinessNatural resource economicsDiversity (politics)AgroforestryEnvironmental planningEconomic growthEcosystemEconomicsEcologyBiologyPolitical science

Abstract

fetched live from OpenAlex

Throughout the Hindu Kush Himalayas, uncultivated plants provide a green social and cultural security to millions of people supporting their livelihood. Review on evaluating the multifunctional role of uncultivated plants in perspective of livelihood support finds that plants add diversity to local food systems, reinforce local culture and contribute diversity to farming systems, and finally are important for household food and nutrition security, social security, income generation and health care. Further, this paper clarifies that local people maintain and conserve diversity for the sake of use. The wise conservation and use of uncultivated plants are essential elements for increasing food security, eliminating poverty, and maintaining the environment. However, the value and potential of uncultivated plants for food and nutrition security, household level health care, income generation opportunity are not yet realized. Fast changing climate and early projections on its impacts suggest that such programmes must increasingly consider the sustenance of ecosystem that promotes uncultivated plants as basis for the welfare of millions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.191

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 head0.184
Teacher spread0.168 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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