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Record W569830108 · doi:10.1186/1710-1492-10-s2-a48

Retracted Article: A post-hoc qualitative analysis of real time heads-up pollen counting versus traditional microscopy counting in the environmental exposure unit (EEU)

2014· article· en· W569830108 on OpenAlexaffvenue
Lisa M. Steacy, Terry J. Walker, Barnaby Hobsbawn, Jenny Thiele, Anne K. Ellis

Post-publication record

NatureRetraction
ReasonDuplication of Content through Error by Journal/Publisher;
Date4/17/2015 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsQueen's UniversityKingston General Hospital
Fundersnot available
KeywordsPollenUnit (ring theory)Computer sciencePsychologyBiologyBotanyMathematics education

Abstract

fetched live from OpenAlex

A custom digital imagery method for real time identification and counting of pollen was qualitatively evaluated in the Environmental Exposure Unit (EEU). Airborne grass pollen was collected in the EEU via a Rotorod® impact sampler. The pollen grains on each sampling rod were counted using both traditional and heads-up microscopy. The heads-up technique incorporated a microscope camera to create an on-screen image of the sampling rod. Firstly, unique images were created by manually advancing the stage, without duplicating previously captured pollen grains. Well-defined, sharp images were obtained by fine focus and zoom combinations to enhance certainty and recognition speed. Secondly, using a custom application, each pollen grain was identified and counted on-screen by “point and click” or “screen touch”, simultaneously counting and permanently anchoring opaque dots to the pollen grain locations. Counts were stored in real time on a central database. Increased clarity of the pollen grains resulted in higher counting accuracy. Duplicate counting of pollen grains was eliminated by digitally labelling counted grains. Additional need for manual counting devices, commonly associated with mechanical and human errors, was eliminated. Error free counts can be obtained with increased speed, therefore, improving the overall efficiency of the process and the EEU system as a whole. This validated heads-up counting technique will allow for an increased response time to changes in the EEU pollen levels. This advancement could also enhance pollen counting processes followed by others using direct microscopy pollen counting techniques.

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)0.007
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.045
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0660.020

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 head0.311
Teacher spread0.289 · 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.

Study designQualitative
DomainMethods
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
Published2014
Admission routes2
Has abstractyes

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