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Record W817481

Generating Random Dynamic Resource Scheduling Problems

2008· article· en· W817481 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

VenueViaţa medicală; revistă de informare profesională şi ştiinţifică a cadrelor medii sanitare · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicResource-Constrained Project Scheduling
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDynamic priority schedulingComputer scienceScheduling (production processes)Fair-share schedulingTwo-level schedulingEarliest deadline first schedulingDistributed computingRate-monotonic schedulingFixed-priority pre-emptive schedulingReal-time computingMathematical optimizationScheduleOperating system
DOInot available

Abstract

fetched live from OpenAlex

Dynamic scheduling refers to a class of scheduling problems in which dynamic events, such as delaying of a task, occur throughout execution. We develop a framework for dynamic resource scheduling imple-mented in Java with a random problem generator, a dy-namic simulator and a scheduler. The problem gen-erator is used to generate benchmark datasets that are read by the simulator, whose purpose is to notify the scheduler of the dynamic events when they occur. We perform a case-study on an oversubscribed dynamic re-source scheduling problem in which we assign unit re-sources to tasks subject to temporal and precedence constraints.

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)0.021
metaresearch head score (Gemma)0.051
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.004
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0050.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.063
GPT teacher head0.340
Teacher spread0.277 · 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