Ocean science educator award announced

Eos ◽  
1993 ◽  
Vol 74 (38) ◽  
pp. 437
Author(s):  
Anonymous
Eos ◽  
1990 ◽  
Vol 71 (33) ◽  
pp. 1034
Author(s):  
Anonymous

Eos ◽  
1990 ◽  
Vol 71 (6) ◽  
pp. 262
Author(s):  
Anonymous

Nature ◽  
2009 ◽  
Author(s):  
Daniel Cressey
Keyword(s):  

2019 ◽  
Author(s):  
Adib Rifqi Setiawan

STEAM is an acronym for Science, Technology, Engineering, Art, Mathematics. STEAM defined as the integration of science, technology, engineering, art, and mathematics into a new cross-disciplinary subject in schools. The concept of integrating subjects in Indonesian schools, generally is not new and has not been very successful in the past. Some people consider STEAM as an opportunity while others view it as having problems. Fenny Roshayanti is science educator and researcher that consider STEAM as an opportunity. She has involved the study of STEAM, as an author, educator, academic advisor, and seminar speaker. This article examines what it has been and continues work from Fenny Roshayanti in the science education. Our exploration uses qualitative methods of narrative approaches in the form of biographical studies. Participants as data sources were selected using a purposive sampling technique which was collected based on retrospective interview and naturalistic observation. Data's validity, reliability, and objectivity checked by using external audit techniques. This work explores the powerful of female’s personal style in developing a form of social influence based on her forms of capital as well as address the positive and negative consequences that may follow while implement and research STEAM in teaching classroom.


Author(s):  
Gyundo Pak ◽  
Yign Noh ◽  
Myong-In Lee ◽  
Sang-Wook Yeh ◽  
Daehyun Kim ◽  
...  

2021 ◽  
Vol 13 (4) ◽  
pp. 744
Author(s):  
J. Xavier Prochaska ◽  
Peter C. Cornillon ◽  
David M. Reiman

We performed an out-of-distribution (OOD) analysis of ∼12,000,000 semi-independent 128 × 128 pixel2 sea surface temperature (SST) regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean’s surface. Our algorithm (ULMO) is a probabilistic autoencoder (PAE), which combines two deep learning modules: (1) an autoencoder, trained on ∼150,000 random cutouts from 2010, to represent any input cutout with a 512-dimensional latent vector akin to a (non-linear) Empirical Orthogonal Function (EOF) analysis; and (2) a normalizing flow, which maps the autoencoder’s latent space distribution onto an isotropic Gaussian manifold. From the latter, we calculated a log-likelihood (LL) value for each cutout and defined outlier cutouts to be those in the lowest 0.1% of the distribution. These exhibit large gradients and patterns characteristic of a highly dynamic ocean surface, and many are located within larger complexes whose unique dynamics warrant future analysis. Without guidance, ULMO consistently locates the outliers where the major western boundary currents separate from the continental margin. Prompted by these results, we began the process of exploring the fundamental patterns learned by ULMO thereby identifying several compelling examples. Future work may find that algorithms such as ULMO hold significant potential/promise to learn and derive other, not-yet-identified behaviors in the ocean from the many archives of satellite-derived SST fields. We see no impediment to applying them to other large remote-sensing datasets for ocean science (e.g., SSH and ocean color).


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