ArticleScientific reports2021
Conservation machine learning: a case study of random forests.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Intelligent Discrimination of Grain Aging Using Volatile Organic Compound Fingerprints and Machine Learning: A Comprehensive Review.Foods (Basel, Switzerland) · 2026Review
- Article
- A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.PloS one · 2025Article
- Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma.PloS one · 2025Article
- Flexible and Stable GaN Piezoelectric Sensor for Motion Monitoring and Fall Warning.Nanomaterials (Basel, Switzerland) · 2024Article
- Assessment of Tail-Cutting in Frozen Albacore (Foods (Basel, Switzerland) · 2024Article
- Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.Scientific reports · 2024Article
- A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion.Bioengineering (Basel, Switzerland) · 2023Review
- Identification of colorectal cancer progression-associated intestinal microbiome and predictive signature construction.Journal of translational medicine · 2023Article
- Using Machine Learning to Evaluate the Role of Microinflammation in Cardiovascular Events in Patients With Chronic Kidney Disease.Frontiers in immunology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Conservation machine learning conserves models across runs, users, and experiments-and puts them to good use. We have previously shown the merit of this idea through a small-scale preliminary experiment, involving a single dataset source, 10 datasets, and a single so-called cultivation method-used to produce the final ensemble. In this paper, focusing on classification tasks, we perform extensive experimentation with conservation random forests, involving 5 cultivation methods (including a novel one introduced herein-lexigarden), 6 dataset sources, and 31 datasets. We show that significant improvement can be attained by making use of models we are already in possession of anyway, and envisage the possibility of repositories of models (not merely datasets, solutions, or code), which could be made available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science.
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What OpenQuestion holds
Registered trials
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.