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Record W1970864408 · doi:10.5558/tfc79475-3

West coast, temperate, old-growth forests

2003· article· en· W1970864408 on OpenAlexaffvenueabout
A. J. Mackinnon

Bibliographic record

VenueThe Forestry Chronicle · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsGovernment of British ColumbiaMinistry of Health
Fundersnot available
KeywordsTemperate rainforestRainforestTemperate climateGeographyOld-growth forestTemperate forestEcologyVegetation (pathology)Montane ecologyCanopyForestryEnvironmental scienceEcosystemBiologyArchaeology

Abstract

fetched live from OpenAlex

Canada's west coast, temperate, old-growth forests include its largest, most commercially valuable, fastest-growing, oldest, and certainly most fought-over forests. They can be divided into three main types: coastal rainforest, coastal subalpine forest, and "rain-shadow" forest. Although there is great variation within each of these broad types, coastal rainforests and subalpine forests share a wet climate and are relatively unimpacted by fire as a stand-replacing disturbance. This allows development of multi-aged, multi-canopy, old-growth forests with large volumes of living and dead wood. These forests are structurally and biologically complex. Coastal rain-shadow forests, on the other hand, have a distinctively drier climate (for the coast), and a history of frequent, low-intensity fires. Although well over half of Canada's original west coast, temperate, old-growth forests remain as old growth, there is great variation ecologically and geographically. In general, the percentage of old-growth forest remaining increases with increasing latitude and elevation. Key words: old growth, old-growth forest, coastal British Columbia, temperate rainforest, protected areas, stand structure

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.004

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

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

Citations39
Published2003
Admission routes3
Has abstractyes

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