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Record W2573043338 · doi:10.1139/cjce-2016-0193

Increased fly ash utilization — value addition through forest road reconstruction

2017· article· en· W2573043338 on OpenAlexvenueno aff
Tomi Kaakkurivaara, Heikki Korpunen

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFly ashAggregate (composite)Work (physics)Environmental scienceBiomass (ecology)ProductivityCost estimateWaste managementEngineeringMaterials scienceGeologyMechanical engineering

Abstract

fetched live from OpenAlex

Increasing forest bioenergy utilization is increasing the need to discover more applications for fly ash to avoid dumping charges. Our study concentrates on defining the work phases of reconstruction work and estimation of construction costs for a method using biomass based fly ash. Cost calculations were carried out for two mixed structures of fly ash and aggregate, two uniform structures of fly ash, and a conventional aggregate structure, where construction material volumes were calculated per kilometre for each structure. Our study defined suitable machines and their productivity per hour for different work phases. Cost calculation equations were formed for the used machines and the transportation of construction materials. Our study showed that building a 250 mm thick uniform layer of fly ash was the best alternative for minimizing construction costs. However, building a 500 mm thick uniform layer of fly ash was the best alternative for minimizing dumping charges.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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 designBench or experimental
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

Citations10
Published2017
Admission routes1
Has abstractyes

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