Deep learning for accurate classification of conifer pollen grains: enhancing species identification in palynology.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

3 citing papers in PubMed.

1. Preliminary Research on the Possibility of Automating the Identification of Pollen Grains in Melissopalynology Using AI, with Particular Emphasis on Computer Image Analysis Methods.Sensors (Basel, Switzerland) · 2026
   Article
2. Morphological diversity of pollen and spores in a human-impacted highland forest-agriculture mosaic in northern Thailand.Scientific reports · 2026
   Article
3. Visual classification of allergenic pollen in iteratively reconstructed lens-less DIHM images.Scientific reports · 2026
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

5 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

Indexed as