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Record W4367322028 · doi:10.18280/mmep.100240

Using Interval Parameters for Latest Start Time and Mission Floats Operation Networks

2023· article· en· W4367322028 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsnot available
Fundersnot available
KeywordsInterval (graph theory)Duration (music)Computer scienceReal-time computingAeronauticsOperations researchGeodesyEnvironmental scienceMeteorologyGeographyEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

In Critical Path Method (CPM), the project managers usually use three quantities; earliest start, latest start and job float for project networking problems.However, interval parameters are used for project scheduling problems dealing with the uncertainties.In this paper, we suggest a new approach for computing the interval time duration of a project networking problem without transforming to crisp equivalent forms.First the interval parameters are represented in their midpoint width form.A new type of arithmetic operations and ranking are applied on the midpoint width form of interval numbers, earliest start, latest start, earliest finish, latest finish and float time are calculated.Interval critical path is identified and the project completion tine is arrived.Numerical description is supplied to describe the method proposed in depth and the results obtained are compared with the existing methods.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.395
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.066
GPT teacher head0.252
Teacher spread0.186 · 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