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Record W91397833 · doi:10.22230/jem.2002v1n2a237

Making sense of site index estimates in British Columbia: A quick look at the big picture

2002· article· en· W91397833 on OpenAlexaboutno aff
Steve Stearns-Smith

Bibliographic record

VenueJournal of Ecosystems and Management · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsSite indexIndex (typography)Site selectionEstimationRange (aeronautics)GeographyProductivityHectareStatisticsMathematicsComputer scienceForestryEconomicsArchaeologyEngineering

Abstract

fetched live from OpenAlex

Site index remains the primary estimate of forest site productivity used throughout British Columbia and around the world. Forest managers often need a better understanding of how various site index estimates are derived in order to effectively apply them in operational settings. Historically, most site index estimates in Canada were derived from the photo-interpreted estimates of stand height and age found in extensive inventories. However, a wider range of data sources and site index tools now make both direct and indirect estimation of site index possible. Consequently, several different site index estimates may exist for any given hectare. The most prominent example involves comparisons of site index estimates derived from natural stand (old-growth) inventories versus the higher estimates frequently observed in post-harvest second-growth stands. These differences can have positive implications for timber supply. An understanding of site tree selection is essential when choosing the best available site index estimate for a given application.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.016
Science and technology studies0.0030.003
Scholarly communication0.0080.005
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2002
Admission routes1
Has abstractyes

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