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Record W3135158445

A New Building in the Hospital – What IT infrastructure should we recommend?

2019· article· en· W3135158445 on OpenAlexaffabout
Mario Ramírez, Erwin Van Hout, Shah Yazdanian, Luiz Costa, Gary Nero

Bibliographic record

VenueCMBES Proceedings · 2019
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsProject teamTeam buildingPhase (matter)BusinessEngineering managementOperations managementEngineeringPublic relationsComputer scienceKnowledge managementPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The Hospital for Sick Children in Toronto, On-tario is embarking in the renovation of the old buildings under the auspices of Project Horizon. The first phase of the project, consist in the development of a 22 story building to house all Patient Support personnel (PSC building). The second phase of the project is to demolish the old parts of the building and build a new Inpatient Care Centre (PCC). A Technology Group was formed to advice the Project Horizon team on the IT infrastruc-ture for the new PSC building. The challenge is to ensure that the IT infrastructure shall meet the needs of PSC’s tenants five year from now. In addition it needs to be flexible to support the needs over the next ten to twenty years. The Technology Group did a thorough research on the pros and cons for Structured Cable and GPON infrastructure. The paper describes the steps the team took to arrive to the final recommendation.

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)0.007
metaresearch head score (Gemma)0.029
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.004
Scholarly communication0.0150.015
Open science0.0020.003
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0300.020

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 head0.083
GPT teacher head0.434
Teacher spread0.351 · 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
GenreOther

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

Citations1
Published2019
Admission routes2
Has abstractyes

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