Evidence map›Paper›PMID 39551852›Full record

ArticleScientific reports2024

Prediction of strata settlement in undersea metal mining based on deep forest.

Weijun Liu, Zida Liu, Zhixiang Liu

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Weijun LiuSchool of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
Zida LiuSchool of Resources and Safety Engineering, Central South University, Changsha, 410083, China. liuzida@csu.edu.cn.
Zhixiang LiuSchool of Resources and Safety Engineering, Central South University, Changsha, 410083, China.

Funding

National Natural Science Foundation of China 52374107
6 · The paper itself

Abstract

Undersea mining encounters challenges due to the presence of seawater. An influx of seawater into stop in undersea can result in enormous disaster. Predicting strata settlement is a crucial measure to ensure the safety of undersea mining. This study proposed an intelligent model based on deep forest (DF) to evaluate the strata settlement during undersea mining. Initially, the strata displacement was monitored in the Xishan mining area of Sanshandao gold mine, China. Comprehensive datasets encompassing roof displacement and twelve influencing factors were compiled from 120 observations. Then, these datasets were statistically analyzed and used to train the DF model. The developed DF model achieved a training R

Indexed as

Deep forestMachine learningStrata settlementUndersea mining

Identifiers

PMID39551852
PMCPMC11570597

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.