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Record W1495012970 · doi:10.22230/jem.2008v9n2a394

An old-growth index for Douglas-fir stands in portions of the Interior Douglas-fir zone, central British Columbia

2008· article· en· W1495012970 on OpenAlexaffabout
O. A. Steen, Richard Dawson, Harold M. Armleder

Bibliographic record

VenueJournal of Ecosystems and Management · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsGovernment of British Columbia
Fundersnot available
KeywordsSeral communityDouglas firBasal areaCanopyIndex (typography)Stand developmentForestryOld-growth forestComplexity indexSite indexGeographyTree (set theory)EcologyMathematicsBiologyArchaeologyEcological successionComputer science

Abstract

fetched live from OpenAlex

We describe a quantitative old-growth index for Douglas-fir (Pseudotsuga menziesii var. glauca) stands in two Interior Douglas-fir (IDF) biogeoclimatic variants (dk3 and dk4) in the central interior of British Columbia. The index uses stand structure data including basal area of very large (≥ 57.5 cm DBH) and large (≥ 37.5 cm DBH) trees, density of small (< 27.5 cm DBH) trees, tree size variability, canopy complexity, density of declining and dead trees, and occurrence of canopy gaps. Three forms of the index were developed to accommodate different objectives and levels of data availability. Index values are grouped into four classes (early seral, mid-seral, mature, and old growth). Qualifiers of these classes provide additional descriptions of old-growth structural development as well as guidance for designing management practices to enhance old-growth development. A link is provided to a Microsoft Excel spreadsheet that can be used to calculate old-growth index values using each form of the index.

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 score0.139
Threshold uncertainty score0.891

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)0.0000.000

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 head0.194
Teacher spread0.189 · 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

Citations2
Published2008
Admission routes2
Has abstractyes

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