Bibliographic record
Abstract
The study of complex multi-faceted scientific questions demand innovative computing solutions—solutions that transcend beyond the management of big data to dedicated semantics-enabled, services-driven infrastructures that can effectively aggregate, filter, process, analyze, visualize and share the cumulative scientific efforts and insights of the research community. From a technical standpoint, E-Science purports technology-enabled collaborative research platforms to (i) collect, store and share multi-modal data collected from different geographic sites, (ii) perform complex simulations and experiments using sophisticated simulation models; (iii) design complex experiments by integrating data and models and executing them as per the experiment workflow; (iv) visualize high-dimensional simulation results; and (v) aggregate and share the scientific results (Fig 1). Taking a knowledge management approach, we have developed an innovative E-Science platform— termed Platform for Ocean Knowledge Management (POKM)—that is built using innovative web-enabled services, services-oriented architecture, semantic web, workflow management and data visualization technologies. POKM offers a suite of E-Science services that allow oceanographic researchers to (a) handle large volumes of ocean and marine life data; (b) access, share, integrate and operationalize the data and simulation models; (c) visualization of data and simulation results; (d) multi-site collaborations in joint scientific research experiments; and (e) form a broad, virtual community of national and international researchers, marine resource managers, policy makers and climate change specialists. (Fig 2) The functional objective of our E-Science infrastructure is to establish an online scientific experimentation platform that supports an assortment of data/knowledge access and processing tools to allow a group of scientists to collaborate and conduct complex experiments by sharing data, models, knowledge, computing resources and expertise. Our E-Science approach is to complement data-driven approaches with domain-specific knowledge-centric models in order to establish causal, associative and taxonomic relations between (a) raw data and modeled observations; (b) observations and their causes; and (c) causes and theoretical models. This is achieved by taking a unique knowledge management approach, whereby we have exploited semantic web technologies to semantically describe the data, scientific models, knowledge artifacts and web services. The use of semantic web technologies provides a mechanism for the selection and integration of problem-specific data from large repositories. To define the functional aspects of the e-science services we have developed a services ontology that provides a semantic description of knowledge-centric e-science services. POKM is modeled along a services-oriented architecture that exposes a range of task-specific web services accessible through a web portal. The POKM architecture features five layers—Presentation Layer, Collaboration Layer, Service Composition Layer, Service Layer and Ontology Layer (Fig 3). POKM is applied to the domain of oceanography to understand our changing eco-system and its impact on marine life. POKM helps researchers investigate (a) changes in marine animal movement on time scales of days to decades; (b) coastal flooding due to changes in certain ocean parameters; (c) density of fish colonies and stocks; and (d) time-varying physical characteristics of the oceans (Fig 4 & 5). In this paper, we present the technical architecture and functional description of POKM, highlighting the various technical innovations and their applications to E-Science.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".