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Record W2507659424

Kinetic Model of CCA Fixation on Wood. Part III. Model Validation

2007· article· en· W2507659424 on OpenAlexfundno aff
Feroz Kabir Kazi, Paul Cooper, Tony Ung

Bibliographic record

VenueWood and Fiber Science (Society of Wood Science and Technology) · 2007
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChromated copper arsenateFixation (population genetics)Isothermal processMaterials scienceChemistryThermodynamicsCopperMetallurgyPhysics
DOInot available

Abstract

fetched live from OpenAlex

In previous studies, models were developed for the initial and main fixation reactions of chromated copper arsenate (CCA-C) on red pine wood as a function of time and isothermal wood temperature conditions following treatment with 1% CCA-C. In this study, these models are used to predict the amount of fixation over sequential short non-isothermal intervals as a way of predicting time to total fixation under variable temperature conditions. The rate of fixation of CCA-C treated red pine pole sections could generally be accurately predicted, using these models, from thermocouple temperature readings in the pole surface, even under a highly variable temperature fixation regime. However, variations in fixation rate were observed even within a single pole, associated with density differences at the butts and tops of the poles. This confirms that fixation time estimates are very sensitive to the model parameters and suggests that the model may not accurately predict fixation rates over a wide range of material sources.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.220
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

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
Published2007
Admission routes1
Has abstractyes

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