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ADS -- correctly identify preprints
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closes  zotero#3047
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@adam3smith
adam3smith committed Jun 9, 2023
1 parent 038892b commit 8265636
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149 changes: 145 additions & 4 deletions ADS Bibcode.js
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Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,7 @@
"priority": 100,
"inRepository": true,
"translatorType": 8,
"browserSupport": "gcsibv",
"lastUpdated": "2021-09-22 03:34:00"
"lastUpdated": "2023-06-09 17:04:40"
}

/*
Expand All @@ -35,6 +34,9 @@
***** END LICENSE BLOCK *****
*/

const preprintType = ZU.fieldIsValidForType('title', 'preprint')
? 'preprint'
: 'report';

// https://github.com/yymao/adstex/blob/64989c9e75d7401ea2b33b546664cbc34cce6a27/adstex.py
const bibcodeRe = /^\d{4}\D\S{13}[A-Z.:]$/;
Expand Down Expand Up @@ -78,7 +80,7 @@ function getTypeFromId(id) {
return "thesis";
}
else if (bibstem.startsWith("arXiv")) {
return "report"; // preprint
return preprintType;
}
else {
// now scan past the bibstem and find the volume number/type abbrev.
Expand Down Expand Up @@ -126,7 +128,9 @@ function scrape(ids) {
item.extra = (item.extra || '') + `\nADS Bibcode: ${id}`;

if (id.slice(4).startsWith('arXiv')) {
item.extra += '\nType: article'; // will map to preprint
if (preprintType == "report") {
item.extra += '\nType: article'; // will map to preprint
}
}

if (item.pages && item.pages.startsWith('arXiv:')) {
Expand Down Expand Up @@ -407,6 +411,143 @@ var testCases = [
"seeAlso": []
}
]
},
{
"type": "search",
"input": {
"adsBibcode": "2023arXiv230604024S"
},
"items": [
{
"itemType": "preprint",
"title": "The FLAMINGO project: cosmological hydrodynamical simulations for large-scale structure and galaxy cluster surveys",
"creators": [
{
"lastName": "Schaye",
"firstName": "Joop",
"creatorType": "author"
},
{
"lastName": "Kugel",
"firstName": "Roi",
"creatorType": "author"
},
{
"lastName": "Schaller",
"firstName": "Matthieu",
"creatorType": "author"
},
{
"lastName": "Helly",
"firstName": "John C.",
"creatorType": "author"
},
{
"lastName": "Braspenning",
"firstName": "Joey",
"creatorType": "author"
},
{
"lastName": "Elbers",
"firstName": "Willem",
"creatorType": "author"
},
{
"lastName": "McCarthy",
"firstName": "Ian G.",
"creatorType": "author"
},
{
"lastName": "van Daalen",
"firstName": "Marcel P.",
"creatorType": "author"
},
{
"lastName": "Vandenbroucke",
"firstName": "Bert",
"creatorType": "author"
},
{
"lastName": "Frenk",
"firstName": "Carlos S.",
"creatorType": "author"
},
{
"lastName": "Kwan",
"firstName": "Juliana",
"creatorType": "author"
},
{
"lastName": "Salcido",
"firstName": "Jaime",
"creatorType": "author"
},
{
"lastName": "Bahé",
"firstName": "Yannick M.",
"creatorType": "author"
},
{
"lastName": "Borrow",
"firstName": "Josh",
"creatorType": "author"
},
{
"lastName": "Chaikin",
"firstName": "Evgenii",
"creatorType": "author"
},
{
"lastName": "Hahn",
"firstName": "Oliver",
"creatorType": "author"
},
{
"lastName": "Huško",
"firstName": "Filip",
"creatorType": "author"
},
{
"lastName": "Jenkins",
"firstName": "Adrian",
"creatorType": "author"
},
{
"lastName": "Lacey",
"firstName": "Cedric G.",
"creatorType": "author"
},
{
"lastName": "Nobels",
"firstName": "Folkert S. J.",
"creatorType": "author"
}
],
"date": "2023-06-01",
"DOI": "10.48550/arXiv.2306.04024",
"abstractNote": "We introduce the Virgo Consortium's FLAMINGO suite of hydrodynamical simulations for cosmology and galaxy cluster physics. To ensure the simulations are sufficiently realistic for studies of large-scale structure, the subgrid prescriptions for stellar and AGN feedback are calibrated to the observed low-redshift galaxy stellar mass function and cluster gas fractions. The calibration is performed using machine learning, separately for three resolutions. This approach enables specification of the model by the observables to which they are calibrated. The calibration accounts for a number of potential observational biases and for random errors in the observed stellar masses. The two most demanding simulations have box sizes of 1.0 and 2.8 Gpc and baryonic particle masses of $1\\times10^8$ and $1\\times10^9 \\text{M}_\\odot$, respectively. For the latter resolution the suite includes 12 model variations in a 1 Gpc box. There are 8 variations at fixed cosmology, including shifts in the stellar mass function and/or the cluster gas fractions to which we calibrate, and two alternative implementations of AGN feedback (thermal or jets). The remaining 4 variations use the unmodified calibration data but different cosmologies, including different neutrino masses. The 2.8 Gpc simulation follows $3\\times10^{11}$ particles, making it the largest ever hydrodynamical simulation run to $z=0$. Lightcone output is produced on-the-fly for up to 8 different observers. We investigate numerical convergence, show that the simulations reproduce the calibration data, and compare with a number of galaxy, cluster, and large-scale structure observations, finding very good agreement with the data for converged predictions. Finally, by comparing hydrodynamical and `dark-matter-only' simulations, we confirm that baryonic effects can suppress the halo mass function and the matter power spectrum by up to $\\approx20$ per cent.",
"extra": "ADS Bibcode: 2023arXiv230604024S",
"libraryCatalog": "NASA ADS",
"shortTitle": "The FLAMINGO project",
"url": "https://ui.adsabs.harvard.edu/abs/2023arXiv230604024S",
"attachments": [
{
"title": "Full Text PDF",
"mimeType": "application/pdf"
}
],
"tags": [
{
"tag": "Astrophysics - Astrophysics of Galaxies"
},
{
"tag": "Astrophysics - Cosmology and Nongalactic Astrophysics"
}
],
"notes": [],
"seeAlso": []
}
]
}
]
/** END TEST CASES **/
13 changes: 9 additions & 4 deletions NASA ADS.js
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Expand Up @@ -9,7 +9,7 @@
"inRepository": true,
"translatorType": 4,
"browserSupport": "gcsibv",
"lastUpdated": "2023-03-20 08:38:43"
"lastUpdated": "2023-06-09 17:06:21"
}

/*
Expand All @@ -35,6 +35,10 @@
***** END LICENSE BLOCK *****
*/

const preprintType = ZU.fieldIsValidForType('title', 'preprint')
? 'preprint'
: 'report';

function getSearchResults(doc) {
const results = doc.querySelectorAll("a[href$=abstract]");
const entries = {};
Expand Down Expand Up @@ -63,7 +67,7 @@ function getTypeFromId(id) {
return "thesis";
}
else if (bibstem.startsWith("arXiv")) {
return "report"; // preprint
return preprintType;
}
else {
// now scan past the bibstem and find the volume number/type abbrev.
Expand Down Expand Up @@ -327,7 +331,7 @@ var testCases = [
"url": "https://ui.adsabs.harvard.edu/abs/2020arXiv201207436Z/abstract",
"items": [
{
"itemType": "report",
"itemType": "preprint",
"title": "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting",
"creators": [
{
Expand Down Expand Up @@ -367,8 +371,9 @@ var testCases = [
}
],
"date": "2020-12-01",
"DOI": "10.48550/arXiv.2012.07436",
"abstractNote": "Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a $ProbSparse$ self-attention mechanism, which achieves $O(L \\log L)$ in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem.",
"extra": "ADS Bibcode: 2020arXiv201207436Z\nType: article",
"extra": "ADS Bibcode: 2020arXiv201207436Z",
"libraryCatalog": "NASA ADS",
"shortTitle": "Informer",
"url": "https://ui.adsabs.harvard.edu/abs/2020arXiv201207436Z",
Expand Down

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