MétaCan
Menu
Back to cohort

Phytosanitation of mountain pine beetle infected lodgepole pine using dielectric fields at radio frequencies

2015· article· es· W2067989254 on OpenAlexafffund
Ciprian Lazarescu, Colette Breuil, Stavros Avramidis

Bibliographic record

VenueMaderas Ciencia y tecnología · 2015
Typearticle
Languagees
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMountain pine beetlePinus contortaPine forestForestryEnvironmental scienceRadio signalGeographyRadio frequencyBiologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

As an environmentally friendly alternative to chemical treatment, this research aimed to establish whether dielectric heating at high frequency of infested lodgepole pine (Pinus contorta) boards and logs, obtained from the mountain pine beetle devastated forests of British Columbia, can result in wood free of living fungi, nematodes and insects. The sample set contained 230 boards, 50x150 and 50x100 mm2 in cross-section and 20 logs, 200-300 mm in diameter; all tested specimens were roughly one meter long. The intention was to test the efficiency of two temperature/time combinations: 56ºC for 30min and 60ºC for 15min that were identified in past works as effective phytosanitary combinations. Data showed that both permutations eradicated all infestation levels and types. The electric field power density per treatment cycle ranged from 23 to 50 kW/m3 and the total heating cycle varied from 42 to 116 minutes for all pest and wood type combinations tested.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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 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

Citations8
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueMaderas Ciencia y tecnologíaSame topicForest Insect Ecology and ManagementFrench-language works237,207