MétaCan
Menu
Back to cohort
Record W6892539751 · doi:10.5281/zenodo.10694261

Substrate Durability Assessment

2024· article· en· W6892539751 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldComputer Science
TopicCurrency Recognition and Detection
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsAgeingMetric (unit)DurabilityBanknoteSubstrate (aquarium)Statistical analysisLead (geology)

Abstract

fetched live from OpenAlex

We present the findings from a comprehensive standardized test conducted on four distinct unprinted banknote substrates. The assessment will primarily focus on evaluating the resistance of these substrates to soiling, based on their performance after undergoing accelerated ageing tests. The accelerated ageing tests applied were aimed to reproduce as close as posible the real circulation conditions. The project encompassed various important components, including the implementation of a gradual accelerated ageing procedure on the samples; comprehensive image analysis from image acquisition to image processing; development of a metric to quantify the level of soiling; statistical analysis of the obtained data and the establishment of a statistical model to estimate the relative mean-life of each substrate; and the interpretation of the results regarding to resistance to soiling and estimated performance in circulation. A key to this project is the Banknote Accelerated Ageing Device (BAAD). This cutting-edge equipment together with the deterioration method employed has proven to be very effective in simulating the soiling and mechanical deterioration observed under real circulation conditions. Overall, the results and analyses presented contribute to a better understanding of the factors influencing substrate performance and can inform decision-making processes in selecting appropriate materials for specific conditions. Another significant finding is that our method showcase the effectiveness in the formally and statistically evaluation of the artificial ageing processes of different substrates. This capability not only saves valuable time but also reduces costs associated with more labor- intensive studies.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.049
GPT teacher head0.285
Teacher spread0.236 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicCurrency Recognition and DetectionFrench-language works237,207