MétaCan
Menu
Back to cohort
Record W2791516256 · doi:10.3390/f9020078

Historic Frequency and Severity of Fire in Whitebark Pine Forests of the Cascade Mountain Range, USA

2018· article· en· W2791516256 on OpenAlexaff
Michael P. Murray, Joel Siderius

Bibliographic record

VenueForests · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsMinistry of Forests
FundersU.S. Forest Service
KeywordsEndangered speciesRange (aeronautics)Fire ecologyForestryGeographyEcologyElevation (ballistics)Environmental scienceEcosystemFire regimePhysical geographyBiologyHabitatEngineering

Abstract

fetched live from OpenAlex

Whitebark pine (Pinus albicaulis Engelm.) is a foundation species of high elevation forest ecosystems in the Cascade Mountain Range of Oregon, Washington, and British Columbia. We examined fire evidence on 55 fire history sites located in the Cascade Range. To estimate dates of historic fires we analyzed 57 partial cross-sections from fire-scarred trees plus 700 increment cores. The resulting 101 fire events indicate fire has been a widespread component of Cascadian whitebark pine stands. Results are site specific and vary considerably. Whitebark pine stands appear to burn in a variety of severities and frequencies. Sites where fire intervals were detected ranged from 9 to 314 years, with a median of 49 years, and averaging 67 years. Fire intervals shortened significantly with higher latitudes. In assessing the most recent fire event at each site, overall, 56 percent burned as stand replacing events. In the 20th century, the number of fires diminished significantly. Due to conservation imperatives, re-introducing fire should be undertaken with extreme care to avoid substantial mortality of this endangered species.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.993

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueForestsSame topicFire effects on ecosystemsFrench-language works237,207