Reference health
Robust ecological analysis of camera trap data labelled by a machine learning model
11 of 30 checkable references need attention · checked 2026-07-22
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
12 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
References needing attention
does not resolve to a known work10.1111/1365‐2664.13602
does not resolve to a known work10.1111/j.1541‐0420.2007.00927.x
does not resolve to a known work10.1007/s10021‐019‐00424‐3
does not resolve to a known work10.1111/2041‐210X.12346
does not resolve to a known work10.1890/02‐3090
does not resolve to a known work10.1890/0012‐9658(2002)083[2248:ESORWD]2.0.CO;2
does not resolve to a known work10.1111/2041‐210X.12278
does not resolve to a known work10.1890/0012‐9658(2006)87[835:GSOMAF]2.0.CO;2
does not resolve to a known work10.1111/2041‐210X.13133
does not resolve to a known work10.1111/2041‐210X.13120
does not resolve to a known work10.1111/2041‐210X.13099
The 19 checked references that resolve
resolves10.1017/S0376892919000298
Wildlife Insights: A Platform to Maximize the Potential of Camera Trap and Other Passive Sensor Wildlife Data for the Planet
resolves10.3897/biss.3.37222
Efficient Pipeline for Automating Species ID in new Camera Trap Projects
resolves10.1073/pnas.1710231115
Iterative near-term ecological forecasting: Needs, opportunities, and challenges
resolves10.1093/biosci/biy068
Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions
resolves10.18637/jss.v043.i10
<b>unmarked</b>: An<i>R</i>Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance
resolves10.1002/rse2.106
Camera‐trapping version 3.0: current constraints and future priorities for development
resolves10.1073/pnas.1719367115
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
resolves10.1002/rse2.132
Camera trapping reveals trends in forest duiker populations in African National Parks
resolves10.1002/ece3.6147
Three critical factors affecting automated image species recognition performance for camera traps
resolves10.1038/sdata.2015.26
Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
resolves10.5281/zenodo.4534108
rcwhytock/gabon_wildlife_training: Public release to accompany paper in MEE
resolves10.5281/zenodo.4486332
rcwhytock/Whytock_and_Swiezewski_et_al_2020: Public release of repository
The 12 references without a DOI — listed, not checked
no DOI — not checkedÄrje J. Raitoharju J. Iosifidis A. Tirronen V. Meissner K. Gabbouj M. Kiranyaz S. &Kärkkäinen S.(2019).Human experts vs. machines in taxa recognition.ArXiv:1708.06899 [Cs q‐Bio Stat]. Retrieved fromhttp://arxiv.org/abs/1708.06899
no DOI — not checkedNotes on the distribution and status of small carnivores in Gabon
no DOI — not checkedGuo C. Pleiss G. Sun Y. &Weinberger K. Q.(2017).On calibration of modern neural networks.ArXiv:1706.04599 [Cs]. Retrieved fromhttp://arxiv.org/abs/1706.04599
no DOI — not checkedcaret: Classification and regression training
no DOI — not checkedKurakin A. Goodfellow I. &Bengio S.(2017).Adversarial examples in the physical world.ArXiv:1607.02533 [cs Stat]. Retrieved fromhttp://arxiv.org/abs/1607.02533
no DOI — not checkedNorouzzadeh M. S. Morris D. Beery S. Joshi N. Jojic N. &Clune J.(2019).A deep active learning system for species identification and counting in camera trap images.ArXiv:1910.09716 [Cs Eess Stat]. Retrieved fromhttp://arxiv.org/abs/1910.09716
no DOI — not checkedOrbell C. &Whytock R. C.(2021).Datasets for Robust ecological analysis of camera trap data labelled by a machine learning model. DataSTORRE: Stirling Online Repository for Research Data. Retrieved fromhttp://hdl.handle.net/11667/170
no DOI — not checkedactivity: Animal activity statistics. R Package v 1.3
no DOI — not checkedSchneider S. Taylor G. W. &Kremer S. C.(2018).Deep learning object detection methods for ecological camera trap data.ArXiv:1803.10842 [Cs]. Retrieved fromhttp://arxiv.org/abs/1803.10842
no DOI — not checkedSmith L. N.(2018).A disciplined approach to neural network hyper‐parameters: Part 1 – Learning rate batch size momentum and weight decay.ArXiv:1803.09820 [Cs Stat]. Retrieved fromhttp://arxiv.org/abs/1803.09820
no DOI — not checkedTan M. &Le Q. V.(2020).EfficientNet: Rethinking model scaling for convolutional neural networks.ArXiv:1905.11946 [Cs Stat]. Retrieved fromhttp://arxiv.org/abs/1905.11946
no DOI — not checkedResNeSt: Split‐attention networks
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