Wood identification of

Weihui Zhan, Bowen Chen, Xiaolian Wu, Zhen Yang, Che Lin, Jinguo Lin, Xin Guan

Abstract read

In one paragraph

Article in Frontiers in plant science, 2023. 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

7 authors.

Weihui ZhanCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Bowen ChenCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Xiaolian WuCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Zhen YangCollege of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Che LinCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Jinguo LinCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Xin GuanCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex. Methods: A wood species identification model based on wood anatomy and using the Results: The SVM model and BPNN model achieved recognition accuracy of 96.4% and 99.6%, respectively, on the real dataset, using the CTGAN-generated vessel dataset. The BPNN model and SVM model achieved recognition accuracy of 75.5% and 77.9% on real dataset, respectively, using the CTGAN-generated wood fiber dataset. Discussion: The machine learning model trained based on the enhanced cell geometric feature data by CTGAN achieved good recognition of

Indexed as

CTGANCyclobalanopsis (Endl.) OerstLIMEmachine learningwood identification

Identifiers

PMID37484454

DOI10.3389/fpls.2023.1203836

PMCPMC10361066

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.