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Record W4393600219 · doi:10.5281/zenodo.6862863

Bark and sapwood allometry for fourteen North American tree species

2022· dataset· en· W4393600219 on OpenAlexaff
Christoforos Pappas, Nicolas Bélanger, Gabriel Bastien-Beaudet, Catherine Couture, Loïc D’Orangeville, Louis Duchesne, Fabio Gennaretti, Daniel Houle, Alexander Hurley, Stefan Klesse, Simon Lebel Desrosiers, Miguel Montoro Girona, Richard L. Peters, Sergio Rossi, Karel St-Amand, Daniel Kneeshaw

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsUniversité du Québec à ChicoutimiEnvironment and Climate Change CanadaUniversité du Québec en Abitibi-TémiscamingueUniversité TÉLUQMinistère des Ressources naturelles et des ForêtsUniversity of New BrunswickUniversité du Québec à Montréal
Fundersnot available
KeywordsAllometryBark (sound)Tree (set theory)ForestryBiologyGeographyMathematicsBotanyEcologyCombinatorics

Abstract

fetched live from OpenAlex

Measurements of sapwood and bark thickness for fourteen North American tree species, based on visual inspection of 651 tree cores. The recorded values include: species name, stem diameter at the breast height (1.3 m above the ground surface; DBH in cm), bark thickness (cm), and sapwood thickness (cm). A detailed documentation of this dataset as well as the site description where the samples were collected are provided in the associated publication (https://doi.org/10.1016/j.agrformet.2022.109092).

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.028

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 head0.031
GPT teacher head0.245
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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