Explainable Deep Learning Framework for Reliable Species-Level Classification Within the Genera

İlknur Meriç Turgut, Dilara Gerdan Koc, Özden Fakıoğlu

Abstract read

In one paragraph

Article in Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

3 authors.

İlknur Meriç TurgutDepartment of Fisheries and Aquaculture Engineering, Faculty of Agriculture, Ankara University, 06110 Ankara, Türkiye.ORCID 0000-0001-7404-5680

Dilara Gerdan KocDepartment of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ankara University, 06110 Ankara, Türkiye.ORCID 0000-0002-2705-299X

Özden FakıoğluDepartment of Basic Science, Faculty of Fisheries, Ataturk University, 25240 Erzurum, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microalgae are an evolutionarily ancient and morphologically diverse group of photosynthetic eukaryotes, with taxonomic resolution complicated by environmentally driven phenotypic plasticity. This study merges deep learning and explainable artificial intelligence (XAI) to establish a transparent, reliable, and biologically meaningful framework for green microalgae (Chlorophyta) classification. Microscope images from three morphologically distinct algal species-

Indexed as

deep learningexplainable artificial intelligence (XAI)Grad-CAMmodel interpretabilityphytoplanktonsaliency maptaxonomic resolution

Identifiers

PMID41514940

DOI10.3390/biology15010099

PMCPMC12785139

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.