MétaCan
Menu
Back to cohort
Record W2044970305 · doi:10.1139/x05-212

Seasonal fire effects on mixed-conifer forest structure and ponderosa pine resin properties

2006· article· en· W2044970305 on OpenAlexvenueno aff
Daniel D. B. Perrakis, James K. Agee

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsPinus <genus>Prescribed burnBark beetleEnvironmental scienceBark (sound)Mountain pine beetleThinningWoody plantForestryPine forestSpring (device)EcologyBiologyGeographyBotany

Abstract

fetched live from OpenAlex

This study examined the effects of spring and fall restoration burning in an old-growth mixed-conifer – ponderosa pine (Pinus ponderosa Dougl. ex P. &amp; C. Laws.) forest in southern Oregon. Variables measured include fuel loads, forest structure indices, mortality of large ponderosa pines, and pine resin defenses. One year after treatment, reductions in surface fuel loads and changes to forest structure parameters suggested that burning treatments could meet restoration objectives, with fall burns being somewhat more effective than spring burns. However, mortality of pre settlement pines was significantly higher in fall burns than in spring burns, and both were higher than in unburned controls. Bark beetles (Coleoptera: Scolytidae) were important mortality agents within 2 years after burning. Resin defenses (pressure and flow) were variable over the 2 years of postburn study but showed no evidence of decrease in burned trees; rather, resin defenses were significantly higher in burned trees than in controls at several measurement dates. While increased beetle attacks have previously been documented following burning, there has been much less research on resin responses to fire. These findings suggest that current models of beetle–host interactions do not properly explain the effects of prescribed fire in ponderosa pine forests.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.990

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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 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

Citations68
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest Insect Ecology and ManagementFrench-language works237,207