Evidence map›Paper›PMID 42877245›Full record

ArticleFrontiers in artificial intelligence2026

Exhaustive feature-subset search for data-driven post-correction of satellite atmospheric processors: a case study on chlorophyll-a retrieval in inland reservoirs.

Victoria Amores-Chaparro, Fernando Broncano, Jesús A Torrecilla-Pinero, Aurora Cuartero

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

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0citing papers in PubMed
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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Victoria Amores-ChaparroDepartment of Computer Systems and Telematics Engineering, School of Technology, Universidad de Extremadura, Cáceres, Spain.
Fernando BroncanoDepartment of Computer Systems and Telematics Engineering, School of Industrial Engineering, Universidad de Extremadura, Badajoz, Spain.
Jesús A Torrecilla-PineroDepartment of Construction, School of Technology, Universidad de Extremadura, Cáceres, Spain.
Aurora CuarteroDepartment of Graphical Expression, School of Technology, Universidad de Extremadura, Cáceres, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Satellite retrieval of chlorophyll-a (Chl-a) is central to monitoring eutrophication in inland reservoirs, yet the atmospheric-correction processors it depends on, trained on oceanic and large-lake conditions, are systematically biased over small water bodies. A data-driven post-correction layer is increasingly used to reduce this bias; for such a layer over the joint output of the C2-Net processors (C2RCC, C2X, and C2XC), we ask which engineered features robustly carry the corrective signal rather than chasing an accuracy headline. Enumerating all 2

Indexed as

chlorophyll-a retrievalexhaustive feature selectionmachine learningout-of-distribution generalizationSentinel-2stability selection

Identifiers

PMID42877245
PMCPMC13662142

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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.