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Record W2117365907 · doi:10.1051/forest:2002043

Optimization of multiple trait selection in western hemlock(<i>Tsuga heterophylla</i> (Raf.) Sarg.) including pulpand paper properties

2002· article· en· W2117365907 on OpenAlexaff
Milosh Ivkovich, Mathew P. Koshy

Bibliographic record

VenueAnnals of Forest Science · 2002
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWestern HemlockPulp (tooth)TsugaSelection (genetic algorithm)Tree breedingTraitAgricultural engineeringComputer scienceBiologyEngineeringBotanyWoody plantMachine learningMedicine

Abstract

fetched live from OpenAlex

Options for incorporating wood quality in British Columbia's hemlock breeding program were investigated. Seventy half-sib families were examined. Attention was given to quantitative variation in tracheid characteristics and its effects on pulp and paper properties. Based on the existing knowledge of relationships between fibre properties and paper quality, the potential gain in yield and wood quality was estimated for different selection strategies. Improvement without much trade-off was possible for volume and tensile strength of pulp and paper. Significant trade-offs would be required to improve the volume, tear strength of paper and strength of mechanical pulp. Therefore, multiobjective optimization would be beneficial. Conservative selection strategies seem realistic, and compromises with gain in volume growth may be profitable. The potential loss arising from the uncertainty about economic values for objectives can be overcome by using different selection indices in multiple breeding populations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.414

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.249
Teacher spread0.174 · 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 designSimulation or modeling
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

Citations12
Published2002
Admission routes1
Has abstractyes

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