Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,004 | 0,010 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,018 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».