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Life cycle prediction for spare parts at IBM spare parts operations

2012· dissertation· en· W28166945 on OpenAlexaboutno aff
L. Bensing

Bibliographic record

VenueJournal of Thermal Biology · 2012
Typedissertation
Languageen
FieldDecision Sciences
TopicForecasting Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSpare partIBMComputer scienceSet (abstract data type)Cluster analysisService (business)Field (mathematics)Order (exchange)Operations researchEngineeringOperations managementArtificial intelligenceMarketingBusinessMathematics

Abstract

fetched live from OpenAlex

Contemporary evolution of thermal physiology has the potential to help limit the physiological stress associated with rapidly changing thermal environments; however it is unclear if wild populations can respond quickly enough for such changes to be effective. We used native Canadian Pumpkinseed (Lepomis gibbosus) sunfish, and non-native Pumpkinseed introduced into the milder climate of Spain ~100 years ago, to assess genetic differences in thermal physiology in response to the warmer non-native climate. We compared temperature performance reaction norms of two Canadian and two Spanish Pumpkinseed populations born and raised within a common environment. We found that Canadian Pumpkinseed had higher routine metabolic rates when measured at seasonally high temperatures (15°C in winter, 30°C in summer), and that Spanish Pumpkinseed had higher critical thermal maxima when acclimated to 30°C in the summer. Growth rates were not significantly different among populations, however Canadian Pumpkinseed tended to have faster growth at the warmest temperatures measured (32°C). The observed differences in physiology among Canadian and Spanish populations at the warmest acclimation temperatures are consistent with the introduced populations being better suited to the warmer non-native climate than native populations. The observed differences could be the result of either founder effects, genetic drift, and/or contemporary adaptive evolution in the warmer non-native climate.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.128
GPT teacher head0.411
Teacher spread0.283 · 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 teacher head, 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

Citations1
Published2012
Admission routes1
Has abstractyes

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