Crossed Tracks: Mesolimulus, Archaeopteryx, and the Nature of Fossils

Organisms leave a variety of traces in the fossil record. Among these traces, vertebrate and invertebrate paleontologists conventionally recognize a distinction between the remains of an organism’s phenotype (body fossils) and the remains of an organism’s life activities (trace fossils). The same convention recognizes body fossils as biological structures and trace fossils as geological objects. This convention explains some curious practices in the classification, as with the distinction between taxa for trace fossils and for tracemakers. I consider the distinction between “parallel taxonomies,” or parataxonomies, which privileges some kinds of fossil taxa as “natural” and others as “artificial.” The motivations for and consequences of this practice are inconsistent. By comparison, I examine an alternative system of classification used by paleobotanists that regards all fossil taxa as “artificially” split. While this system has the potential to inflate the number of taxa with which paleontologists work, the system offers greater consistency than conventional practices. Weighing the strengths and weaknesses of each system, I recommend that paleontologists should adopt the paleobotanical system more broadly

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

HaloDaSH: The Deep and Shallow History of Aquatic Life\u27s Passages between Marine and Freshwater Habitats

This series of papers highlights research into how biological exchanges between salty and freshwater habitats have transformed the biosphere. Life in the ocean and in freshwaters have long been intertwined; multiple major branches of the tree of life originated in the oceans and then adapted to and diversified in freshwaters. Similar exchanges continue to this day, including some species that continually migrate between marine and fresh waters. The series addresses key themes of transitions, transformations, and current threats with a series of questions: When did major colonizations of fresh waters happen? What physiographic changes facilitated transitions? What organismal characteristics facilitate colonization? Once a lineage has colonized freshwater, how frequently is there a return to the sea? Have transitions impelled diversification? How do organisms adapt physiologically to changes in halohabitat, and are such adaptive changes predictable? How do marine and freshwater taxa differ in morphology? How are present-day global changes in the environment influencing halohabitat and how are organisms contending with them? The purpose of the symposium and the papers in this volume is to integrate findings at multiple levels of biological organization and from disparate fields, across biological and geoscience disciplines

Using Machine Learning to Classify Extant Apes and Interpret the Dental Morphology of the Chimpanzee-human Last Common Ancestor

Machine learning is a formidable tool for pattern recognition in large datasets. We developed and expanded on these methods, applying machine learning pattern recognition to a problem in paleoanthropology and evolution. For decades, paleontologists have used the chimpanzee as a model for the chimpanzee-human last common ancestor (LCA) because they are our closest living primate relative. Using a large sample of extant and extinct primates, we tested the hypothesis that machine learning methods can accurately classify extant apes based on dental data. We then used this classification tool to observe the affinities between extant apes and Miocene hominoids. We assessed the discrimination accuracy of supervised learning algorithms when tasked with the classification of extant apes (n=175), using three types of data from the postcanine dentition: linear, 2-dimensional, and the morphological output of two genetic patterning mechanisms that are independent of body size: molar module component (MMC) and premolar-molar module (PMM) ratios. We next used the trained algorithms to classify a sample of fossil hominoids (n=95), treated as unknowns. Machine learning classifies extant apes with greater than 92% accuracy with linear and 2-dimensional dental measurements, and greater than 60% accuracy with the MMC and PMM ratios. Miocene hominoids are morphologically most similar in dental size and shape to extant chimpanzees. However, relative dental proportions of Miocene hominoids are more similar to extant gorillas and follow a strong trajectory through evolutionary time. Machine learning is a powerful tool that can discriminate between the dentitions of extant apes with high accuracy and quantitatively compare fossil and extant morphology. Beyond detailing applications of machine learning to vertebrate paleontology, our study highlights the impact of phenotypes of interest and the importance of comparative samples in paleontological studies

The Puzzling Inventory of Life

In this paper I introduce the concept of biodiversity by means of its peculiar story and defend the importance of biodiversity as an autonomous object of scientific enquiry (Sections 1 and 2). I then discuss two difficulties, (i) the lack of an agreed definition and (ii) the elusiveness of the notion of biodiversity. While I argue that (i) is a problem that can be handled, I suggest that (ii) follows from the vagueness of the concept of diversity and its cognate kind (Sections 3 and 4). Yet, the notion of kind, connecting biodiversity to taxonomy, indicates the path to follow in order to explore what biodiversity is, even though it is a way full of hurdles to overcome (Sections 5 and 6)

The Future of General Systems Research: Obstacles, Potentials, Case Studies

This paper attempts to provide an evaluative and prescriptive overview of the young field of systems science as exemplified by one of its 'specialties' general systems theory (GST). Subjective observation and some data on seven vital signs are presented to measure the progress of the field over the last two decades. Thirty-three specific obstacles inhibiting current research in systems science are presented. Suggestions for overcoming these obstacles are cited as a prescription for improved progress in the field. A sampling of some of the potential near-term developments that may be expected in the three rather distinct areas of research on systems isomorphics, improvement of systems methodologies, and the utility of systems applications are illustrated with mini-case studies. Throughout, there is an attempt to identify 'key' questions and practical mechanisms that might serve as a stimulus for research. Finally, a set of criteria defining a general theory of systems is suggested and illustrated with a case study. The paper concludes with a projection of the long-term contributions that systems science may make toward a resolution of the growing chasm between high-tech solutions and high-value needs in human systems

The Simple Knowledge Organization System (SKOS): a situation report for the HIVE Project

HIVE (Helping Interdisciplinary Vocabularies Engineering) es un proyecto financiado por el IMLS (Institute of Museums and Library Services), e indirectamente, en Dryad, ambos proyectos en colaboración del Metadata Research Center y el National Evolutionary Synthesis Center (NESCent) in Durham, North Carolina. Con el desarrollo de HIVE se pretende resolver esta problemática mediante una propuesta de generación automática de metadatos que permita la integración dinámica de vocabularios controlados específicos. Para asistir la integración de vocabularios se seleccionó SKOS (Simple Knowledge Organisation System), un estándar del World Wide Web Consortium (W3C) para la representación de sistemas de organización del conocimiento o vocabularios, como tesauros, esquemas de clasificación, sistemas de encabezamiento de materias y taxonomías, en el marco de la Web Semántica.El presente informe realiza un análisis exhaustivo de la situación en cuanto a la aplicación de SKOS. El estudio incluye una detallada revisión de literatura científica y recursos web sobre el modelo, una selección de los proyectos, iniciativas, herramientas, grupos de investigación claves y cualquier otro tipo de información que pudiera ser de relevancia para el logro de los objetivos del proyecto HIVE. Asimismo, se analiza la importancia de SKOS para el logro de la interoperabilidad semántica y se elaboran un conjunto de recomendaciones para los miembros del proyecto HIVE