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Record W3133112288

Hardwood CLT Program in Southern and Central Ontario

2021· article· en· W3133112288 on OpenAlexaboutno aff
LI Xin-Cheng

Bibliographic record

VenueTSpace · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness
DOInot available

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT) is a large prefabricated solid engineering plank made of multiple layers of planks glued together and it is primarily used in structures such as the floors, walls, and roofs of buildings. ANSI/APA PRG 320 is the world recognized CLT lumber production standard, and the main raw material of CLT has always been softwood rather than hardwood. However, the bending strength, compressive strength, and shear strength of hardwood CLT lumber are stronger than softwood CLT lumber. The large and underutilized hardwood resources in central and southern Ontario provide a huge resource advantage for the hardwood CLT project. This article uses the Cost-Benefit Assessments model to assess the feasibility of investing in hardwood CLT plants in central and southern Ontario. The results show that the payback period of the hardwood CLT factory is 5 years, and the rate of return on investment of 10 years, 15 years, and 20 years are all-around 11%. This study could strengthen investor confidence and it also identifies the direction for the development of hardwood CLT plants in central and southern Ontario.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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