Evidence map›Paper›PMID 38626806›Full record

ArticleJournal of the Royal Society, Interface2024

Probing the complexity of wood with computer vision: from pixels to properties.

Mirko Lukovic, Laure Ciernik, Gauthier Müller, Dan Kluser, Tuan Pham, Ingo Burgert, Mark Schubert

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Society, Interface, 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

7 authors.

Mirko LukovicLaboratory for Cellulose & Wood Materials, WoodTec Group, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.ORCID 0000-0003-0694-1621
Laure CiernikDepartment of Computer Science, ETH Zürich-Swiss Federal Institute of Technology, 8092 Zurich, Switzerland.
Gauthier MüllerLaboratory for Cellulose & Wood Materials, WoodTec Group, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Dan KluserDepartment of Computer Science, ETH Zürich-Swiss Federal Institute of Technology, 8092 Zurich, Switzerland.
Tuan PhamDepartment of Computer Science, ETH Zürich-Swiss Federal Institute of Technology, 8092 Zurich, Switzerland.
Ingo BurgertLaboratory for Cellulose & Wood Materials, WoodTec Group, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Mark SchubertLaboratory for Cellulose & Wood Materials, WoodTec Group, Empa-Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.ORCID 0000-0002-6900-0370

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We use data produced by industrial wood grading machines to train a machine learning model for predicting strength-related properties of wood lamellae from colour images of their surfaces. The focus was on samples of Norway spruce (

Indexed as

PiceaWoodcomplex materialscomputer visionmachine learningpattern formationwood properties

Identifiers

PMID38626806
PMCPMC11023017

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.