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Record W2033608342 · doi:10.1139/x01-059

Genetic variation in the decay resistance of Scots pine wood against brown rot fungus

2001· article· en· W2033608342 on OpenAlexvenueno aff
Anni Harju, Martti Venäläinen, Egbert Beuker, Pirkko Velling, Hannu Viitanen

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsScots pinePinus <genus>HorticultureHeritabilityBotanyCoefficient of variationGenetic variationBiologyAnimal scienceChemistry

Abstract

fetched live from OpenAlex

The role of genotype in the durability of Scots pine (Pinus sylvestris L.) wood against decay by brown rot fungus (Coniophora puteana (Schum. ex Fr.) Karst. (strain Bam EBW 15)) was studied in a laboratory test. The wood material was obtained from 32-year-old half-sib progenies of Scots pine. The increment core samples of sapwood and juvenile heartwood were decayed using a modification of the standardized EN 113 method. The mean densities of the sapwood and heartwood samples were 391 and 337 mg·cm–3, respectively, and the mean mass losses were 114 and 80 mg·cm–3, respectively. The additive genetic components were small compared with the total phenotypic variance, which resulted in small narrow-sense heritabilities in mass loss. The most marked feature was the wide phenotypic variation in mass loss observed in heartwood (range 199 mg·cm–3) compared with sapwood (range 72 mg·cm–3) samples. Low heritability, together with the relatively high coefficient of additive genetic variation (CVA) in heartwood mass loss, suggests that advances in breeding can only be made through intensive testing in the environments which the studied experiment represents.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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

Citations26
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicPlant Pathogens and Fungal DiseasesFrench-language works237,207