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Record W2012632249 · doi:10.1139/x07-224

Canadian national biomass equations: new parameter estimates that include British Columbia data

2008· article· en· W2012632249 on OpenAlex
Chhun-Huor Ung, Pierre Y. Bernier, Xiaojing Guo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsDiameter at breast heightTree allometryBiomass (ecology)Data setTree (set theory)ForestryAllometryEnvironmental scienceEcologyGeographyMathematicsStatisticsBiologyCombinatorics

Abstract

fetched live from OpenAlex

National allometric equations covering the most common tree species of Canada’s forests were produced based on tree mass data acquired in the early 1980s during the ENergy from the FORest (ENFOR) program. The equations allow us to calculate the mass estimate of four tree components (foliage, branches, stem bark, and stem wood) using either diameter at breast height or a combination of diameter at breast height and height. Missing from that data set, however, were the data from British Columbia. A usable British Columbia data set was finally found and has now been incorporated into the national data set. Here, we present revised allometric equations for six species covered in the previous work and also found in the British Columbia data set as well as for the “hardwoods”, “softwoods”, and “all species” equations. New equations are also provided for eight species specific to the British Columbia data.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.114
GPT teacher head0.322
Teacher spread0.208 · 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