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Record W411575363 · doi:10.17528/cifor/002519

Riches of the forest: food, spices, crafts and resins of Asia [Japanese]

2008· book· en· W411575363 on OpenAlexfundno aff
Charles D. Lopez, Patrícia Shanley, eds.

Bibliographic record

VenueCenter for International Forestry Research (CIFOR) eBooks · 2008
Typebook
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
FundersFP7 International CooperationInternational Fund for Agricultural DevelopmentConsortium of International Agricultural Research CentersCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungEuropean CommissionOverseas Development InstituteInternational Development Research CentreTinker FoundationNature ConservancyInstitut Alam Sekitar dan Pembangunan, Universiti Kebangsaan MalaysiaInternational Tropical Timber OrganizationMargot Marsh Biodiversity FoundationDepartment for International DevelopmentUnited Nations Educational, Scientific and Cultural OrganizationJohn D. and Catherine T. MacArthur Foundation
KeywordsForestryPulp and paper industryFood scienceTraditional medicineBusinessGeographyChemistryEngineeringMedicine

Abstract

fetched live from OpenAlex

This book contains 20 case studies that explain how a selection of forest resources featuring forest plans, animals and fungi are harvested, processed and traded. The botanical cases are presented according to the main part of the plant being used - the fruit, bark or resin. Sometimes the plants have multiple uses, or different cultures may use the same part of a particular plant in different ways. Animals and animal products that require forest habitat are also critical for rural livelihoods, and are represented in this volume by edible bird's nests and insect larvae. In each case, the book describes the main characteristics of the forest product, its historical usage, harvesting and management, and how it is processed and traded. In closing, each author comments briefly on trends and current issues regarding the resource. The final chapter reviews common themes and lessons that can be drawn from these cases.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.003

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 head0.044
GPT teacher head0.292
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2008
Admission routes1
Has abstractyes

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