Showing 4851 - 4900 of 5831 Items
Bowdoin Orient, v. 126, no. 6
Date: 1995-10-20
Access: Open access
- There are two volumes numbered 126. This is the original.
Bowdoin Orient, v. 126, no. 19
Date: 1996-04-05
Access: Open access
- There are two volumes numbered 126. This is the original.
Bowdoin Orient, v. 126, no. 18
Date: 1996-03-08
Access: Open access
- There are two volumes numbered 126. This is the original.
Bowdoin Orient, v. 132, no. [11]
Date: 2002-12-01
Access: Open access
- There are two volumes numbered 132. This is the original.
Bowdoin Orient, v. 132, no. 10
Date: 2000-11-17
Access: Open access
- There are two volumes numbered 132. This is the original.
Bowdoin Orient, v. [128], no. 10
Date: 1997-11-21
Access: Open access
- This volume was misnumbered as volume 126.
Bowdoin Orient, v. 132, no. 4
Date: 2000-09-29
Access: Open access
- There are two volumes numbered 132. This is the original.
Bowdoin Orient, v. 132, no. 21
Date: 2001-04-13
Access: Open access
- There are two volumes numbered 132. This is the original.
Bowdoin Orient, v. 132, no. 19
Date: 2001-03-09
Access: Open access
- There are two volumes numbered 132. This is the original.
It's All Under Control: Essays Access to this record is restricted to members of the Bowdoin community. Log in here to view.
Date: 2020-01-01
Creator: Jack Tarlton
Access: Access restricted to the Bowdoin Community
Word Embedding Driven Concept Detection in Philosophical Corpora
Date: 2020-01-01
Creator: Dylan Hayton-Ruffner
Access: Open access
- During the course of research, scholars often explore large textual databases for segments of text relevant to their conceptual analyses. This study proposes, develops and evaluates two algorithms for automated concept detection in theoretical corpora: ACS and WMD retrieval. Both novel algorithms are compared to key word retrieval, using a test set from the Digital Ricoeur corpus tagged by scholarly experts. WMD retrieval outperforms key word search on the concept detection task. Thus, WMD retrieval is a promising tool for concept detection and information retrieval systems focused on theoretical corpora.
Linking genotype to phenotype in a changing ocean: inferring the genomic architecture of a blue mussel stress response with genome-wide association
Date: 2018-03-01
Creator: S. E. Kingston, P. Martino, M. Melendy, F. A. Reed, D. B., Carlon
Access: Open access
- A key component to understanding the evolutionary response to a changing climate is linking underlying genetic variation to phenotypic variation in stress response. Here, we use a genome-wide association approach (GWAS) to understand the genetic architecture of calcification rates under simulated climate stress. We take advantage of the genomic gradient across the blue mussel hybrid zone (Mytilus edulis and Mytilus trossulus) in the Gulf of Maine (GOM) to link genetic variation with variance in calcification rates in response to simulated climate change. Falling calcium carbonate saturation states are predicted to negatively impact many marine organisms that build calcium carbonate shells – like blue mussels. We sampled wild mussels and measured net calcification phenotypes after exposing mussels to a ‘climate change’ common garden, where we raised temperature by 3°C, decreased pH by 0.2 units and limited food supply by filtering out planktonic particles >5 μm, compared to ambient GOM conditions in the summer. This climate change exposure greatly increased phenotypic variation in net calcification rates compared to ambient conditions. We then used regression models to link the phenotypic variation with over 170 000 single nucleotide polymorphism loci (SNPs) generated by genotype by sequencing to identify genomic locations associated with calcification phenotype, and estimate heritability and architecture of the trait. We identified at least one of potentially 2–10 genomic regions responsible for 30% of the phenotypic variation in calcification rates that are potential targets of natural selection by climate change. Our simulations suggest a power of 13.7% with our study's average effective sample size of 118 individuals and rare alleles, but a power of >90% when effective sample size is 900.