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Record W7043443908

Testing proposal for an optically tracked CMM (OTCMM) in a pre-normative context

2016· article· en· W7043443908 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Metrology Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware portabilityFlexibility (engineering)Context (archaeology)Quality (philosophy)Product (mathematics)Function (biology)Standardization
DOInot available

Abstract

fetched live from OpenAlex

The current sets of internationally-recognized standards and some national guidelines in the area of coordinate measurement systems (CMS) represent a fundamental way to ensure better communication about a system’s specifications between users and manufacturers. The documentary and physical standards produced to date help solve part of an issue faced by companies that integrate contact or non-contact three-dimensional (3D) coordinate measurement machines (CMM) into their production pipeline: deciding in which technology to invest. Quality CMMs typically involve a significant investment when considering the cost of equipment, training, software, and maintenance contracts over the functional lifetime of a given system or systems, notwithstanding the requirements of the global nature of manufacturing activities. With recent interests in portable CMMs, documentary and physical standards have attracted the attention of industrial users and the technical community at large. Both tethered like articulated arm CMM (AACMM) and untethered like optically tracked CMM (OTCMM) systems have emerged from being nice to have to the must have 3D equipment on the shop floor. Portability and flexibility for in-process product measurements and verification are displacing more traditional methods that require fixed gantries especially when OTCMM are combined with vibration tracking ancillary devices that can dampen the effects of typical shop floor on the quality of 3D measured coordinates.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.035
GPT teacher head0.276
Teacher spread0.241 · 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 designBench or experimental
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

Citations0
Published2016
Admission routes1
Has abstractyes

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