Retracted Article: A post-hoc qualitative analysis of real time heads-up pollen counting versus traditional microscopy counting in the environmental exposure unit (EEU)
Post-publication record
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
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.020 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".