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Record W2807638255 · doi:10.1177/2277977918774647

A Case in Medical Equipment Design for Strategic Sustainability

2018· article· en· W2807638255 on OpenAlexaff
Hanuv Mann, Inder Jit Singh Mann, Nehul Gullaiya

Bibliographic record

VenueSouth Asian Journal of Business and Management Cases · 2018
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsCarleton UniversityUniversity of Winnipeg
Fundersnot available
KeywordsSustainabilityProduct (mathematics)BusinessOutcome (game theory)Risk analysis (engineering)Medical equipmentProcess managementEnvironmentally friendlyComputer scienceOperations managementManufacturing engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

The alignment of strategic sustainability goals can be very challenging for industries where stringent product requirements restrict the ability to innovate environmentally friendly alternatives. This case studies an instance of medical equipment being designed to increase functionality and decrease disposability, creating greater utility. The primary outcome of this research is a demonstration that cost is not prohibitive when introducing reusable medical tools and equipment when innovative solutions satisfy stringent standards and can provide superior functionality.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0070.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.029
GPT teacher head0.268
Teacher spread0.239 · 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 designNot applicable
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

Citations4
Published2018
Admission routes1
Has abstractyes

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