MétaCan
Menu
Back to cohort
Record W2912991756 · doi:10.1139/cjfr-2018-0282

Effect of the degree of decay on the electrical resistance of wood degraded by brown-rot fungi

2019· article· en· W2912991756 on OpenAlexvenueno aff
Shan Gao, Xiaoquan Yue, Lihai Wang

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsDegree (music)MyceliumWater contentElectrical resistance and conductanceMoistureResistance (ecology)BotanyHorticultureChemistryMaterials scienceComposite materialBiologyAgronomy

Abstract

fetched live from OpenAlex

There have been limited efforts to investigate the association between wood decay and electrical resistance; consequently, we have examined the change in the electrical resistance of wood progressively decayed by brown-rot fungi to elucidate the effect of the degree of decay. The rate of mass loss of wood was used as an indicator of the degree of fungal decay. The changes in the moisture content and ion concentrations were measured at various decay stages. The results showed a significant difference in the electrical resistances of sound wood and fungal-treated wood after 24 weeks. The electrical resistance significantly decreased with the exposure time. The degree of decay increased as the mass loss increased, resulting in the severe breakdown of cell walls and the accumulation of fungal mycelia. Empirical models related to the rate of mass loss and the percent decrease in the electrical resistance were established. The moisture content and cation concentrations increased to various degrees in decayed wood. This increase in the cation concentration was considered to play a dominant role in the decrease in the electrical resistance. The study results provide valuable information for developing an electrical resistance based method coupled with ion content measurements for incipient wood decay detection.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.254
Teacher spread0.216 · 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 designBench or experimental
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

Citations25
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicWood Treatment and PropertiesFrench-language works237,207