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Record W7132995807

Predicting the early age temperature response of concrete using isothermal calorimetry

2007· dissertation· W7132995807 on OpenAlexfundaboutno aff
Michael Edward Robbins

Bibliographic record

VenueTSpace · 2007
Typedissertation
Language
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsothermal processThermalCalorimeter (particle physics)Mass concreteThermal analysisSlag (welding)Experimental dataCalorimetryThermal conduction
DOInot available

Abstract

fetched live from OpenAlex

A simplified thermal simulation tool was developed for predicting the temperature rise due to hydration within concrete elements including those that contain slag or fly ash. The purpose of the program is to perform a rough thermal analysis without requiring detailed technical inputs. The simulation is designed to perform its analysis on a 2-D section through the element using a finite difference method, taking into account surface convection and conduction to adjacent materials, as well as solar radiation. An experimental program involving isothermal heat-conduction calorimeter testing was used to help populate the model with data for cementing materials commonly available in Ontario, including several Portland cements and a number of blast-furnace slags and fly ashes at various replacement levels. To verify the simulation results, results from the simulation will be compared to the results of two different field trials involving mass concretes with high levels of SCM replacement.

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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.313
Teacher spread0.290 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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