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Record W2035375036 · doi:10.1016/j.imic.2014.11.001

Ensuring food security in the small islands of Maluku: A community genebank approach

2014· article· en· W2035375036 on OpenAlexaff
Semuel Leunufna, M. M. Evans

Bibliographic record

VenueJournal of Marine and Island Cultures · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsFood securityAgricultureDiversity (politics)Agricultural biodiversityBiodiversityOrder (exchange)Genetic resourcesCropBusinessGeographyAgroforestryAgricultural scienceGenetic diversityEnvironmental planningEnvironmental resource managementAgricultural economicsBiotechnologyEcologyForestryBiologyEconomicsPolitical science

Abstract

fetched live from OpenAlex

As a province composed of hundreds of small islands, Maluku is highly susceptible to decreasing biodiversity of plant resources for agriculture. Pressures from pests and disease infestations, difficulty of seed storage, market demands for specific cultivars, and the introduction of new superior varieties are decreasing crop and plant genetic diversity and will impact the food security in the islands. The establishment of community genebanks is proposed to ensure the continuing existence of plant genetic resources and thus the food security of the islands of Maluku Province. The development of facilities and training of personnel to support the survey, collection, and conservation of materials is required, in part to facilitate their cycling of the crop/plant materials to farmers in need. Also required is the study of role and the problems faced by farmers in order to propose supports for bio-diversity that maybe sociological as well as technological.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.018
GPT teacher head0.190
Teacher spread0.172 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of Marine and Island CulturesSame topicGenetic and Environmental Crop StudiesFrench-language works237,207