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Record W3214017216 · doi:10.22230/jem.2021v21n1a611

Comparison of Two Treatment Regimes for Managing Western Balsam Bark Beetle

2021· article· en· W3214017216 on OpenAlexaff
Lorraine Maclauchlan, Julie E. Brooks

Bibliographic record

VenueJournal of Ecosystems and Management · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsKamloops Art Gallery
Fundersnot available
KeywordsBalsamAbies lasiocarpaBark beetleBark (sound)LoggingForestryPopulationPicea engelmanniiBiologyFellingMontane ecologyEcologyGeographyHorticulture

Abstract

fetched live from OpenAlex

The efficacy of pheromone-baited, standing subalpine fir Abies lasiocarpa (Hook) Nutt. and felled green trap trees was tested in southern British Columbia as potential manage- ment techniques for containing western balsam bark beetle Dryocoetes confusus Swaine populations prior to logging. In the year treatments were deployed, standing trees in close proximity to baited trees had significantly higher levels of current attack than those near felled trap trees or in control blocks. The control blocks had the lowest level of current at- tack. Diameters of attacked trees were significantly greater than unattacked trees in all treatments. Naturally attacked, standing subalpine fir had high levels of occupation (number of nuptial galleries) along the full length of the bole. Baited trees had similar levels of occupancy up to six metres in height. Felled green trees had lower occupancy than the baited or naturally attacked trees. Although baited trees concentrated attack into a discrete area, they did not artificially trigger an outbreak or further population expansion in the year following treatment. Felled trap trees appeared less attractive to western balsam bark beetle than natural, susceptible, standing subalpine fir; they are more difficult to de- ploy and therefore not recommended as a means of containing western balsam bark beetle prior to logging.

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: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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