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Record W2786251549

Tropical Timber Atlas: Technological characteristics and uses

2017· book· en· W2786251549 on OpenAlexaboutno aff
Jean Gérard, Daniel Guibal, Sébastien Paradis, Jean-Claude Cerre, Isabelle Châlon, Marie-France Thévenon, Anne Thibaut, Loïc Brancheriau, Gérard Gandon, Alban Guyot, Patrick Langbour, Sylvain Lotte, R. Marchal, Patrick Martin, Bernard Thibaut, Michel Vernay, Nadine Amusant, Christine Baudassé, Nabila Boutahar, Brigitte Cabantous, Catherine Gérard, Cathy Méjean, Sylvie Mouras, Nathalie Troalen, Michèle Vialle, Ghislaine Volle, Alba Zaremski, Henri Baillères, Jacques Beauchêne, Fernand Boyer, Gilles Calchera, Kévin Candelier, Claude Daigremont, Daniel Fouquet, Philippe Gallet, Soepe Koese, Nicolas Leménager, Luc J. Martin, Alfredo Napoli, Luc Pignolet, François Pinta, Jean‐Marc Roda, Christian Sales, Pierre Valière

Bibliographic record

VenueAgritrop (Cirad) · 2017
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)GeographyTemperate climateForestryProduct (mathematics)Wood industryEngineeringArchaeologyEcologyMathematicsBiology
DOInot available

Abstract

fetched live from OpenAlex

This atlas presents technical information for professionals who process and use temperate or tropical timber. lt combines the main technical characteristics of 283 tropical species and 17 species from temperate regions most commonly used in Europe with their primary uses. Each data sheet is accompanied by two photos of sawn wood (flat sawn and quarter sawn, or flat sawn and half quarter sawn), two macro photographs, and for certain species, an illustration of how the wood can be used. This publication will be most useful to opera1tors in the wood industry, including producers (forest managers, operating companies, political decision-makers) and consumers (importers, traders, processors, purchasers, architects, main contractors and builders). The Atlas serves as a tool of reference for teaching and training in the forest and wood sectors in tropical regions. Its purpose is to promote the most appropriate uses for each species according to its characteristics and in line with the motto: "the right wood in the right place". This book was produced by the Wood team of CIRAD's BioWooEB Research Unit with financial support from the International Tropical Timber Organization (ITIO) and Agropolis Fondation. It is the product of thirty years of research in wood technology science, provided by numerous contributors. It was coordinated using version 7 of Tropix, released by CIRAD.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.650
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.211
Teacher spread0.185 · 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 designNot applicable
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

Citations19
Published2017
Admission routes1
Has abstractyes

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