title: Fingerprints and age models for widespread New Zealand tephra marker beds erupted since 30,000 years ago: a framework for NZ-INTIMATE

authors: Lowe, David J.

subjects: tephra

abstract: The role of tephras in the NZ-INTIMATE project is a critical one because most high-resolution palaeoclimatic records are linked and dated by one or more tephra layers. In this review, first we document eruptive, distributional, and compositional fingerprinting data, both mineralogical and geochemical, for 22 key marker tephras erupted since 30,000 years ago to facilitate their identification and correlation. We include new glass compositional data. The selected marker tephras comprise 10 from Taupo and nine from Okataina volcanoes (rhyolitic), one from Tuhua volcano (peralkaline rhyolitic), and one each from Tongariro and Egmont volcanoes (andesitic). Second, we use four approaches to develop 2σ-age models for the tephras (youngest to oldest): (1) calendar ages for Kaharoa and Taupo/Y were obtained by wiggle-matching log-derived tree-ring sequences dated by 14C; (2) Whakaipo/V was dated using an age–depth model from peat; (3) 14 tephras in the montane Kaipo peat sequence (Waimihia/S, Unit K, Whakatane, Tuhua, Mamaku, Rotoma, Opepe/E, Poronui/C, Karapiti/B, Okupata, Konini, Waiohau, Rotorua, Rerewhakaaitu) were dated by simultaneously wiggle-matching stratigraphic position and 51 independent 14C-age points against IntCal04 using Bayesian probability methods via both OxCal and Bpeat; and (4) the five oldest tephras, erupted before ca 18,000 cal. yr BP, were dated by calibrating limited numbers of 14C ages using IntCal04 (Okareka) or comparison curves of the expanded Cariaco Basin sequence (Te Rere, Kawakawa/Oruanui, Poihipi, Okaia). Kawakawa/Oruanui tephra, the most widely distributed marker tephra, was erupted probably ca 27,097±957 cal. yr BP. Potential dating approaches for the older tephras include their identification in Antarctic ice cores (if present) or annually laminated sediments for which robust calendar-age models have been constructed, high-precision AMS 14C dating on appropriate material from environmentally stable sites, systematic luminescence dating, or new radiometric techniques (e.g. U–Th/He) if suitable minerals are available and errors markedly reduced. Further application of Bayesian age-modelling to stratigraphic sequences of 14C ages, possibly augmented with luminescence ages, may help refine age models for pre-Holocene tephras with the largest errors. Finally, we discuss the critical role these marker tephras play in the ongoing construction of an event stratigraphy for the New Zealand region, which is a key objective of Australasian and Southern Hemisphere INTIMATE projects.

text: Fingerprints and age models for widespread New Zealand tephra marker beds erupted since 30,000 years ago: a framework for NZ-INTIMATE The role of tephras in the NZ-INTIMATE project is a critical one because most high-resolution palaeoclimatic records are linked and dated by one or more tephra layers. In this review, first we document eruptive, distributional, and compositional fingerprinting data, both mineralogical and geochemical, for 22 key marker tephras erupted since 30,000 years ago to facilitate their identification and correlation. We include new glass compositional data. The selected marker tephras comprise 10 from Taupo and nine from Okataina volcanoes (rhyolitic), one from Tuhua volcano (peralkaline rhyolitic), and one each from Tongariro and Egmont volcanoes (andesitic). Second, we use four approaches to develop 2σ-age models for the tephras (youngest to oldest): (1) calendar ages for Kaharoa and Taupo/Y were obtained by wiggle-matching log-derived tree-ring sequences dated by 14C; (2) Whakaipo/V was dated using an age–depth model from peat; (3) 14 tephras in the montane Kaipo peat sequence (Waimihia/S, Unit K, Whakatane, Tuhua, Mamaku, Rotoma, Opepe/E, Poronui/C, Karapiti/B, Okupata, Konini, Waiohau, Rotorua, Rerewhakaaitu) were dated by simultaneously wiggle-matching stratigraphic position and 51 independent 14C-age points against IntCal04 using Bayesian probability methods via both OxCal and Bpeat; and (4) the five oldest tephras, erupted before ca 18,000 cal. yr BP, were dated by calibrating limited numbers of 14C ages using IntCal04 (Okareka) or comparison curves of the expanded Cariaco Basin sequence (Te Rere, Kawakawa/Oruanui, Poihipi, Okaia). Kawakawa/Oruanui tephra, the most widely distributed marker tephra, was erupted probably ca 27,097±957 cal. yr BP. Potential dating approaches for the older tephras include their identification in Antarctic ice cores (if present) or annually laminated sediments for which robust calendar-age models have been constructed, high-precision AMS 14C dating on appropriate material from environmentally stable sites, systematic luminescence dating, or new radiometric techniques (e.g. U–Th/He) if suitable minerals are available and errors markedly reduced. Further application of Bayesian age-modelling to stratigraphic sequences of 14C ages, possibly augmented with luminescence ages, may help refine age models for pre-Holocene tephras with the largest errors. Finally, we discuss the critical role these marker tephras play in the ongoing construction of an event stratigraphy for the New Zealand region, which is a key objective of Australasian and Southern Hemisphere INTIMATE projects.

year: 2008-01

title: Transferring sentiment knowledge between words and tweets

authors: Bravo-Marquez, Felipe

subjects: computer science

abstract: Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.

text: Transferring sentiment knowledge between words and tweets Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.

year: 2018

title: Te mātauranga whakatere waka

authors: Tuaupiki, Jackie Whetumarama

abstract: Hei tēnei tuhinga e whai ake nei, ka tirohia te hangarau waka a ngā tūpuna o Te Moana nui a Kiwa i whakawhiti ai rātou ki ō rātou moutere tae atu ki te mātauranga whakatere waka me te arotahi o te titiro ki te whakatinanatanga o aua mātauranga. Ka tāmutu i konā, ka pūhia te titiro i runga i ngā huarere o Poronīhia e kite ai tātou i ngā āhuatanga o Tāwhirimātea i ōna moutere me ngā wero ki te whakatere waka i ērā huarere.

text: Te mātauranga whakatere waka Hei tēnei tuhinga e whai ake nei, ka tirohia te hangarau waka a ngā tūpuna o Te Moana nui a Kiwa i whakawhiti ai rātou ki ō rātou moutere tae atu ki te mātauranga whakatere waka me te arotahi o te titiro ki te whakatinanatanga o aua mātauranga. Ka tāmutu i konā, ka pūhia te titiro i runga i ngā huarere o Poronīhia e kite ai tātou i ngā āhuatanga o Tāwhirimātea i ōna moutere me ngā wero ki te whakatere waka i ērā huarere.

year: 2017