Geoscience Australia AusPIX user interface API v0.9
Notice bibliographique
Résumé
Spatially Linked-data, built using the Discrete Global Grid System (DGGS) as a tool. These functions provide statistical cross-referencing between features of dissimilar geographic layers, to expresses statistical relationships between them. Can be applied to point, line, polygon and raster datasets (including Digital Earth Australia - DEA data). <br>This API is located at https://api.dggs.ga.gov.au/docs and contains several functions the user can access. The data drill function is the most commonly used for determining the features at a specific location.<br>Where appropriate, these tools calculate the apportionment figure which calculates the percentage that one feature is spatially within a comparison features from another geography. ABS, GA and other agencies use this sort of information to apportion data from one geography to another (e.g. to attribute Local Government Areas (LGA) polygons with data collected on ABS SA2 polygons).<br>There are many other use-cases. For example, tell me how many residential addresses are with in a wildfire burn area. Which LGA is the fire is within, which State Electorate, which suburbs, and which postcodes.<br>All this information is available from AusPIX web user interfaces, without the need to open a GIS package. <br>This AusPIX DGGS solution is built into a fast-API web interface (known also as a swagger interface) and resides inside Geoscience Australia (GA) infrastructure (on AWS). The fast-API is a modern method to share information through a user web-interface, providing secure access in both human and machine readable forms. This is F.A.I.R technology.<br>Humans can web-click through the API to find and copy the information they need. Machines can also query the API to consume the information for any higher level dashboards and other APIs. <br>This API is available at https://api.dggs.ga.gov.au/docs and has received an average of 100 hits (invocations or uses) per month over the last 6 months, which is quite good considering it is still waiting to be advertised in eCat. The most used function at the moment is the dataDrill function. Users input a Latitude/Longitude location and receive back a useful set of information about that location. Other functions are available and several potential ones identified.<br>Hyperlinks in the data also provide the landing pages to provide mapped features, geometry, and metadata from the GA/ABS semantically linked datasets and their APIs.<br>A feature of how the system is built is the ability to cross-reference any combination required, without the need to wait for re-calculation. The AusPIX system has this flexibility because its base-geography is equal area DGGS cells provisioned as a intelligent raster. This raster is provided as a rather simple SQL table for any APIs to query. All this technology is hidden from the end-user.<br>Because the DGGS cells and their attributed values are pre-calculated, the system works at high speed.<br>AusPIX provides a unique service beyond map data. Rather AusPIX focuses on the individual features and their relationships to features in other datasets. The benefit is that much of the difficult map interpretation or analysis is provided in completed form for the user. Rather than providing just data, AusPIX automates the provision of the next level up - information and statistics.<br>
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,011 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,291 |
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 ».