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Record W2472947119 · doi:10.1039/c6ay90099b

Correction: Simplifying microfluidic separation devices towards field-detection of blood parasites

2016· article· en· W2472947119 on OpenAlexaff
Stefan H. Holm, Jason P. Beech, Michael P. Barrett, Jonas O. Tegenfeldt

Bibliographic record

VenueAnalytical Methods · 2016
Typearticle
Languageen
FieldComputer Science
TopicDigital Imaging for Blood Diseases
Canadian institutionsInstitute of Infection and Immunity
FundersNanoLund, Lunds UniversitetKnut och Alice Wallenbergs StiftelseLunds UniversitetCrafoordska StiftelsenVetenskapsrådetRoyal Society of ChemistryRoyal SocietyWellcome Trust
KeywordsMicrofluidicsField (mathematics)Separation (statistics)Computer scienceChromatographyNanotechnologyChemistryMaterials scienceMathematicsMachine learning

Abstract

fetched live from OpenAlex

Correction for ‘Simplifying microfluidic separation devices towards field-detection of blood parasites’ by S. H. Holm et al. , Anal. Methods , 2016, 8 , 3291–3300.

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)0.005
metaresearch head score (Gemma)0.089
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.089
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.003
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0050.003
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0850.069

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 head0.032
GPT teacher head0.387
Teacher spread0.354 · 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
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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