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Record W1507437593 · doi:10.13001/uwnpsrc.2005.3627

Allometric Model Development in Lodgepole Pine Forests of the Greater Yellowstone Ecosystem

2005· article· en· W1507437593 on OpenAlexaboutno aff
Daniel B. Tinker, Rick Arcano

Bibliographic record

VenueThe UW National Parks Service Research Station Annual Reports · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsPinus contortaTree allometryBiomass (ecology)AllometryEcosystemEnvironmental scienceEcologyLatitudeForestryGeographyBiology

Abstract

fetched live from OpenAlex

Allometric equations for estimating above­ and belowground biomass of lodgepole pine have been developed in Alberta, Canada, southeastern British Columbia, southeastern WY, and in Washington and Oregon (Johnstone 1971; Comeau and Kimmins 1989; Pearson et al. 1984; Gholz et al. 1979, respectively). More recently, allometric equations for young lodgepole pine saplings have also been developed in Yellowstone National Park (YNP) for aboveground biomass by Turner et al. (2004), and for belowground biomass by Litton et al. (2003). However, because of variability in latitude, growing conditions, substrate and climate, existing equations that predict biomass for mature lodgepole pine trees are not appropriate for use in the Greater Yellowstone Ecosystem (GYE), and new allometric equations specific for the GYE are needed. In this study, we will develop new allometric equations for predicting above- and belowground biomass in mature lodgepole pine forests of the GYE.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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 designSimulation or modeling
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

Citations0
Published2005
Admission routes1
Has abstractyes

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