Evidence map›Paper›PMID 42736260›Full record

ArticleWater environment research : a research publication of the Water Environment Federation2026

Tree-Based Machine Learning Models for Predicting Phosphate Adsorption Performance of Layered Double Hydroxides: Insights Into Mechanisms and Process Optimization.

Jianpeng Jiang, Yingjun Xiong

Abstract read
In one paragraph

Article in Water environment research : a research publication of the Water Environment Federation, 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

2 authors.

Jianpeng JiangCollege of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0005-8931-4392
Yingjun XiongCollege of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Optimizing layered double hydroxides (LDHs) for phosphate removal is challenged by complex, multivariable interactions. To bridge this knowledge gap, this study introduces a novel data-driven framework leveraging a comprehensive 2251-record dataset and advanced machine learning to predict and optimize LDHs adsorption capacity. Among six tree-based models evaluated, CatBoost achieved the best overall performance across five random seeds, with a test R

Indexed as

HydroxidesMachine LearningPhosphatesWater Pollutants, ChemicalAdsorptionBoosting Machine Learning AlgorithmsPrediction AlgorithmsPredictive Learning ModelsWater PurificationHydroxidesPhosphatesWater Pollutants, Chemicallayered double hydroxidesmachine learningphosphate adsorption

Identifiers

PMID42736260
PMCPMC13574928

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.