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Record W2056886472 · doi:10.1155/2008/589532

Advanced Secure Information Retrieval Technology for Multilayer Information Extraction

2008· article· en· W2056886472 on OpenAlexaff
Shoude Chang, Kui Yu, Jiaren Liu

Bibliographic record

VenueJournal of Nanomaterials · 2008
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsSteacie Institute for Molecular SciencesInstitute for Microstructural Sciences
Fundersnot available
KeywordsMaterials scienceDocumentationAuthentication (law)Computer scienceQuantum dotNanotechnologyComputer security

Abstract

fetched live from OpenAlex

Secure information retrieval technology aims at status identification and documentation authentication. Ideally, materials or devices used in these technologies should be hard to find, difficult to counterfeit, and as simple as possible. This manuscript addresses a novel information retrieval technology, with photoluminescent (PL) semiconductor quantum dots (QDs) synthesized via wet chemistry approaches used as its coding materials. Conceptually, these QDs are designed to exhibit emission at Fraunhofer line positions, namely, black lines in the solar spectrum; thus, the retrieval system can extract useful information under sunshine covering areas. Furthermore, multiphoton excitation (MPE) technology enables the retrieval system to be multilayer information extraction, with thin films consisting of QDs applied to various substrates, such as military helmets and vehicle and fingernails. Anticipated applications include security, military, and law enforcement. QD‐based security information can be easily destroyed by preset expiration in the presence of timing agents.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.084
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.005
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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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