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Sökning: WFRF:(Jones Ashley R.) > Stockholms universitet

  • Resultat 1-4 av 4
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1.
  • Downey, Harriet, et al. (författare)
  • Training future generations to deliver evidence-based conservation and ecosystem management
  • 2021
  • Ingår i: Ecological Solutions and Evidence. - : Wiley. - 2688-8319. ; 2:1
  • Forskningsöversikt (refereegranskat)abstract
    • 1. To be effective, the next generation of conservation practitioners and managers need to be critical thinkers with a deep understanding of how to make evidence-based decisions and of the value of evidence synthesis.2. If, as educators, we do not make these priorities a core part of what we teach, we are failing to prepare our students to make an effective contribution to conservation practice.3. To help overcome this problem we have created open access online teaching materials in multiple languages that are stored in Applied Ecology Resources. So far, 117 educators from 23 countries have acknowledged the importance of this and are already teaching or about to teach skills in appraising or using evidence in conservation decision-making. This includes 145 undergraduate, postgraduate or professional development courses.4. We call for wider teaching of the tools and skills that facilitate evidence-based conservation and also suggest that providing online teaching materials in multiple languages could be beneficial for improving global understanding of other subject areas.
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2.
  • Forslund, Tommie, et al. (författare)
  • El Apego Va a Juicio: Problemas de Custodia y Protección Infantil : [Attachment goes to court: Child protection and custody issues]
  • 2021
  • Ingår i: Anuario de psicología jurídica. - : Colegio Oficial de la Psicologia de Madrid. - 1133-0740 .- 2174-0542. ; 32:1, s. 115-139
  • Tidskriftsartikel (refereegranskat)abstract
    • Attachment theory and research are drawn upon in many applied settings, including family courts, but misunderstandings are widespread and sometimes result in misapplications. The aim of this consensus statement is, therefore, to enhance understanding, counter misinformation, and steer family-court utilisation of attachment theory in a supportive, evidence-based direction, especially with regard to child protection and child custody decision-making. This article is divided into two parts. In the first part, we address problems related to the use of attachment theory and research in family courts, and discuss reasons for these problems. To this end, we examine family court applications of attachment theory in the current context of the best-interest-of-the-child standard, discuss misunderstandings regarding attachment theory, and identify factors that have hindered accurate implementation. In the second part, we provide recommendations for the application of attachment theory and research. To this end, we set out three attachment principles: the child's need for familiar, non-abusive caregivers; the value of continuity of good-enough care; and the benefits of networks of attachment relationships. We also discuss the suitability of assessments of attachment quality and caregiving behaviour to inform family court decision-making. We conclude that assessments of caregiver behaviour should take center stage. Although there is dissensus among us regarding the use of assessments of attachment quality to inform child custody and child-protection decisions, such assessments are currently most suitable for targeting and directing supportive interventions. Finally, we provide directions to guide future interdisciplinary research collaboration.
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3.
  • Hosseinzadeh, Griffin, et al. (författare)
  • Photometric Classification of 2315 Pan-STARRS1 Supernovae with Superphot
  • 2020
  • Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 905:2
  • Tidskriftsartikel (refereegranskat)abstract
    • The classification of supernovae (SNe) and its impact on our understanding of explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification-connecting SN light curves back to their spectroscopically defined classes. Here, we present Superphot, an open-source Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class.
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4.
  • Villar, V. Ashley, et al. (författare)
  • SuperRAENN : A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae
  • 2020
  • Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 905:2
  • Tidskriftsartikel (refereegranskat)abstract
    • Automated classification of supernovae (SNe) based on optical photometric light-curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multiwavelength follow-up, as well as archival population studies. Here we present the complete sample of 5243 SN-like light curves (in g(P1)r(P1)i(P1)z(P1)) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters, and depth, making this a useful training set for the community. Using this data set, we train a novel semisupervised machine learning algorithm to photometrically classify 2315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of an RF supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I) and macro-averaged purity and completeness of 66% and 69%, respectively. We find the highest accuracy rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Our complete spectroscopically and photometrically classified samples break down into 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects).
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  • Resultat 1-4 av 4

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