Merging the citations received by arXiv-deposited e-prints and their corresponding published journal articles: Problems and perspectives

Gao, Yong; Wu, Qiang; Zhu, Linna

doi:10.1016/j.ipm.2020.102267

Cited by 16 publications

(34 citation statements)

References 43 publications

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“…Down to metallicities of [Fe/H]∼ − 1 dex this offset appears metallicityindependent which sheds new light on the famous disagreement between predicted Li abundance and the one measured in cool, old, metal-poor on the Spite plateau of A(Li)∼2.3 dex (Spite & Spite 1982). In particular, Gao et al (2020) speculate that the most metal-poor stars on the warm side of the dip may have experienced insignificant Li depletion as well as insignificant Galactic Li enrichment, naturally explaining why their abundances closely reflect those predicted by standard Big Bang nucleosynthesis.…”

Section: Galactic and Stellar Chemical Evolutionmentioning

confidence: 74%

“…Thanks to the hundreds of thousands of Li measurements, we are also able to study phenomena which previously have mainly been analysed in cluster stars, such as the occurrence of the Li dip (Boesgaard & Tripicco 1986), a region among the warm dwarf stars, for which deep mixing induced by rotation and meridional circulation causes strong Li depletion. The first analysis of this region with GALAH+ DR3 by Gao et al (2020) has identified a significant offset between the warm and cool side of this Li dip of 0.4−0.5 dex. Down to metallicities of [Fe/H]∼ − 1 dex this offset appears metallicityindependent which sheds new light on the famous disagreement between predicted Li abundance and the one measured in cool, old, metal-poor on the Spite plateau of A(Li)∼2.3 dex (Spite & Spite 1982).…”

Section: Galactic and Stellar Chemical Evolutionmentioning

confidence: 98%

See 1 more Smart Citation

The GALAH Survey: second data release

Buder

Asplund

Duong

et al. 2018

Monthly Notices of the Royal Astronomical Society

360

447

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The Galactic Archaeology with HERMES (GALAH) survey is a large-scale stellar spectroscopic survey of the Milky Way and designed to deliver chemical information complementary to a large number of stars covered by the Gaia mission. We present the GALAH second public data release (GALAH DR2) containing 342,682 stars. For these stars, the GALAH collaboration provides stellar parameters and abundances for up to 23 elements to the community. Here we present the target selection, observation, data reduction and detailed explanation of how the spectra were analysed to estimate stellar parameters and element abundances. For the stellar analysis, we have used a multi-step approach. We use the physics-driven spectrum synthesis of Spectroscopy Made Easy (SME) to derive stellar labels (T eff , log g, [Fe/H], [X/Fe], v mic , v sin i, A K S ) for a representative training set of stars. This information is then propagated to the whole survey with the data-driven method of The Cannon. Special care has been exercised in the spectral synthesis to only consider spectral lines that have reliable atomic input data and are little affected by blending lines. Departures from local thermodynamic equilibrium (LTE) are considered for several key elements, including Li, O, Na, Mg, Al, Si, and Fe, using 1D stellar atmosphere models. Validation tests including repeat observations, Gaia benchmark stars, open and globular clusters, and K2 asteroseismic targets lend confidence to our methods and results. Combining the GALAH DR2 catalogue with the kinematic information from Gaia will enable a wide range of Galactic Archaeology studies, with unprecedented detail, dimensionality, and scope.

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Section: Galactic and Stellar Chemical Evolutionmentioning

confidence: 74%

Section: Galactic and Stellar Chemical Evolutionmentioning

confidence: 98%

The GALAH Survey: second data release

Buder

Asplund

Duong

et al. 2018

Monthly Notices of the Royal Astronomical Society

360

447

View full text Add to dashboard Cite

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“…On another note, citation count consolidation between preprints and associated publications (Gao et al. 2020 ) depends on a comprehensive identification of all preprint–publication links. A more accurate picture of preprint–publication links is needed to reassess the increasing role of preprints in contemporary science communication.…”

Section: Discussionmentioning

confidence: 99%

“… 2020 ; Gao et al. 2020 ). Aforementioned caveats were raised as preprint servers failed to signal publication DOIs exhaustively (Abdill and Blekhman 2019 ; Fraser et al.…”

Section: Related Work On Preprint–publication Linkingmentioning

confidence: 99%

Day-to-day discovery of preprint–publication links

2021

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Preprints promote the open and fast communication of non-peer reviewed work. Once a preprint is published in a peer-reviewed venue, the preprint server updates its web page: a prominent hyperlink leading to the newly published work is added. Linking preprints to publications is of utmost importance as it provides readers with the latest version of a now certified work. Yet leading preprint servers fail to identify all existing preprint–publication links. This limitation calls for a more thorough approach to this critical information retrieval task: overlooking published evidence translates into partial and even inaccurate systematic reviews on health-related issues, for instance. We designed an algorithm leveraging the Crossref public and free source of bibliographic metadata to comb the literature for preprint–publication links. We tested it on a reference preprint set identified and curated for a living systematic review on interventions for preventing and treating COVID-19 performed by international collaboration: the COVID-NMA initiative (covid-nma.com). The reference set comprised 343 preprints, 121 of which appeared as a publication in a peer-reviewed journal. While the preprint servers identified 39.7% of the preprint–publication links, our linker identified 90.9% of the expected links with no clues taken from the preprint servers. The accuracy of the proposed linker is 91.5% on this reference set, with 90.9% sensitivity and 91.9% specificity. This is a 16.26% increase in accuracy compared to that of preprint servers. We release this software as supplementary material to foster its integration into preprint servers’ workflows and enhance a daily preprint–publication chase that is useful to all readers, including systematic reviewers. This preprint–publication linker currently provides day-to-day updates to the biomedical experts of the COVID-NMA initiative.

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“…This concatenation or addition usually suffers from checkerboard artifacts [8], which greatly limit the capabilities of the network in generating smooth outputs on semantic segmentation. To overcome this problem, the Pixel De-convolutional layer (PDL) [9] was proposed to replace the up-sampling layers. The U-Net with the original up-sampling layers replaced with the PDLs is called Pixel-Deconvolutional-Net (PDN).…”

Section: Introductionmentioning

confidence: 99%

Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

Yao¹,

Chang²

2021

IJMPCERO

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Purpose: To improve the liver auto-segmentation performance of threedimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features. Methods: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers. The hypothesis of this study was that the segmentation quality of the liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL). Seventy-eight plans of abdominal cancer patients were anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen plans were used to test the performance. The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks. Results: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks. The segmentation performance was evaluated using the remaining 16 cases. For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN. Conclusion: The PDN has outperformed the U-Net on liver auto-segmentation. The predicted contours of PDN are more conformal and smoother when compared with the U-Net.

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Merging the citations received by arXiv-deposited e-prints and their corresponding published journal articles: Problems and perspectives

Cited by 16 publications

References 43 publications

The GALAH Survey: second data release

The GALAH Survey: second data release

Day-to-day discovery of preprint–publication links

Auto-Segmentation on Liver with U-Net and Pixel De-Convolutional Network

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