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Record W2108484033 · doi:10.1287/ited.1080.0008

An Interactive Spreadsheet-Based Tool to Support Teaching Design of Experiments

2008· article· en· W2108484033 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINFORMS Transactions on Education · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceFactor (programming language)Set (abstract data type)Replication (statistics)SoftwareSimple (philosophy)Product (mathematics)Programming languageStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper describes an interactive spreadsheet-based tool that can be used to generate data representative of the type that might be obtained running a structured set of experiments. The purpose of this tool is to help the user experience the iterative nature of design and analysis of experiments. The tool supports quick and simple generation of data for one and two-factor problems. The underlying relationships are based on queuing approximations for a single-stage batch production environment. Factor levels are related to product lot sizes and the response is assumed to be average lot flowtimes. Variability due to replication is emulated by sampling from a statistical distribution. Statistical software packages can be used to generate linear or quadratic models from the results generated. Analysis can include the examination of main and interaction effects or the optimization of lot sizes to minimize flowtimes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.141
GPT teacher head0.471
Teacher spread0.330 · 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