Spatial Visualization of Publicly Accessible Species Occurrence Data
Notice bibliographique
Résumé
The Coastal and Ocean Information Network Atlantic’s (COINAtlantic) mission is to promote, facilitate and influence information management, policies and programs that enhance Integrated Coastal and Ocean Management (ICOM) in Atlantic Canada. A project to support this mission has been underway since 2014 with principle funding from the Atlantic Ecosystem Initiative (Environment and Climate Change Canada) and in the last year also from the Department of Fisheries and Ocean to rescue and make accessible to the public and other researchers, species occurrence data using the Ocean Biogeographic Information System Canada (OBIS Canada) Information Publishing Tool (OBIS Canada IPT). Over 300 data resources of marine species occurrences were identified and cataloged and prioritized for processing according to OBIS standards. Using on-line tools developed by COINAtlantic and modified for this project, the data resources published by the project to the OBIS Canada IPT and all the other public data resources were made available as internet searchable web mapping services. Once a day, a scripted process interrogates the OBIS Canada IPT data resources and builds an Open Geospatial Consortium (OGC) Web Mapping Service using MapServer (http://mapserver.org), one map layer for each data resource, and a KML file that provides a bounding box and linkages to the full data resource and metadata on the OBIS Canada IPT. MapServer is an Open Source platform for publishing spatial data and interactive mapping applications to the web. The KML files are searchable on the internet and the WMS is available for use by any capable GIS system with access to the internet. A customized version of the COINAtlantic Search Utility (CSU) (http://coinatlantic.tools/csu/?mapset=acmsd2015) has been developed to visualize the map layers generated by the process (see Fig. 1. The CSU is a search engine which uses the Google Search API to crawl its index for related spatial data in KML and WMS (Web Mapping Service) format, and then displays the results (Boudreau 2014). The CSU also generates an internal data base of WMS and KML spatial data resources from the search results. This data base can be searched as an alternative to searching with the Google Search API (there are over 3,400 records in the data base pointing to remote geospatial services). This customized CSU uses map legends for each map layer that are automatically generated by the process described above and permits the interrogation of any species occurrence location to see the full IPT data record for that location. The CSU enables the user to add any other WMS that is found by using the tool’s search function or known to the user so that the species occurrence data can be viewed in its spatial and / or environmental context. Future developments include the possible deployment of the process to IPTs other than the OBIS Canada IPT, the publication of OGC compliant Web Feature Services that would permit the user to stylize and analyze the data in their own GIS environment, the improvement of the legends generated by the automatic legend generation process, expanding the richness of the metadata displayed in the “Additional Layer Information” window, and exposing the CSU’s internal data base to internet searches using the OGC compliant standard Catalog Service on the Web (CSW).
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,001 | 0,021 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».