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Record W38059850 · doi:10.1002/smll.202306682

Access to the Local AIDS Service Organization among African, Caribbean and Other Black Residents of Middlesex-London, Ontario, Canada

2014· article· en· W38059850 on OpenAlexfundaboutno aff
Soraya Blot

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftCanadian Institutes of Health ResearchCarl-Zeiss-Stiftung
KeywordsService (business)GeographyPolitical scienceBusinessMarketing

Abstract

fetched live from OpenAlex

The popularity of two-photon direct laser writing in biological research is remarkable as this technique is capable of 3D fabrication of microstructures with unprecedented control, flexibility and precision. Nevertheless, potential impurities such as residual monomers and photoinitiators remaining unnoticed from the photopolymerization in the structures pose strong challenges for biological applications. Here, the first use of high-precision 3D microstructures fabricated from a one-component material system (without monomers and photoinitiators) as a 3D cell culture platform is demonstrated. The material system consists of prepolymers with built- in crosslinker motieties, requiring only aliphatic C, H units as reaction partners following two-photon excitation. The material is written by direct laser writing using two-photon processes in a solvent-free state, which enables the generation of structures at a rapid scan speed of up to 500 mm s<sup>-1</sup> with feature sizes scaling down to few micrometers. The generated structures possess stiffnesses close to those of common tissue and demonstrate excellent biocompatibility and cellular adhesion without any additional modification. The demonstrated approach holds great promise for fabricating high-precision complex 3D cell culture scaffolds that are safe in biological environments.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.207
Teacher spread0.188 · 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 designObservational
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
Published2014
Admission routes2
Has abstractyes

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