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Record W2030592468 · doi:10.4236/mi.2013.23008

Nuclear Track Detectors for Relativistic Nuclear Fragmentation Studies: Comparison with Other Competitive Techniques

2013· article· en· W2030592468 on OpenAlexfundno aff
Mukhtar Ahmed Rana, G. Sher, S. Manzoor, F. B. Malik, Kanwal Naz

Bibliographic record

VenueModern Instrumentation · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsnot available
FundersRijksuniversiteit GroningenNational University of SingaporeUniversity of AlbertaCERN
KeywordsNuclear trackDetectorProjectileFragmentation (computing)Nuclear physicsPhysicsNuclear powerTrack (disk drive)Nuclear engineeringOpticsComputer scienceEngineering

Abstract

fetched live from OpenAlex

The potential of the high resolution nuclear track detector (NTD) CR-39 is examined carefully for the measurement of relativistic nuclear projectile fragmentation cross sections and studies of related processes using the experience of many years of such measurements. The charge resolution and the charge resolving power of CR-39 detectors for the measurements of 158 A GeV 207Pb projectiles and their fragments are presented. Exposures of target-detector stacks, the chemical etching procedure and the nuclear track measurements are described in detail discussing precautions and possible errors. The procedures discussed are also valid for other NTDs. A comparison with electronic active detectors is also made considering important detection and measurement aspects. An experimental design proposing the co-use of NTDs with in-use active detectors is described.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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 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

Citations1
Published2013
Admission routes1
Has abstractyes

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