Articles | Volume 5, issue 1
https://doi.org/10.5194/gchron-5-109-2023
https://doi.org/10.5194/gchron-5-109-2023
Short communication/technical note
 | 
10 Mar 2023
Short communication/technical note |  | 10 Mar 2023

Technical note: colab_zirc_dims: a Google Colab-compatible toolset for automated and semi-automated measurement of mineral grains in laser ablation–inductively coupled plasma–mass spectrometry images using deep learning models

Michael C. Sitar and Ryan J. Leary

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gchron-2022-12', Simon Nachtergaele, 01 Jun 2022
    • AC1: 'Reply on RC1', Michael Sitar, 22 Jul 2022
  • RC2: 'Comment on gchron-2022-12', Taryn Scharf, 12 Jun 2022
    • AC2: 'Reply on RC2', Michael Sitar, 22 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (08 Aug 2022) by Pieter Vermeesch
AR by Michael Sitar on behalf of the Authors (14 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (16 Dec 2022) by Pieter Vermeesch
RR by Nikki Seymour (02 Feb 2023)
ED: Publish as is (03 Feb 2023) by Pieter Vermeesch
ED: Publish as is (06 Feb 2023) by Klaus Mezger (Editor)
AR by Michael Sitar on behalf of the Authors (16 Feb 2023)  Author's response   Manuscript 
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Short summary
We developed code to automatically and semi-automatically measure dimensions of detrital mineral grains in reflected-light images saved at laser ablation–inductively coupled plasma–mass spectrometry facilities that use Chromium targeting software. Our code uses trained deep learning models to segment grain images with greater accuracy than is achievable using other segmentation techniques. We implement our code in Jupyter notebooks which can also be run online via Google Colab.