Canada GEESE 2: Visualization of Integrated Marine Geoscience Data for Canadian and Proximal Waters
Bibliographic record
Abstract
The Geological Survey of Canada has made most of its holdings of marine geoscience data available online with unrestricted access. These holdings constitute the premier collections of geological and geophysical source data for Canadian and proximal waters. Multibeam bathymetric imagery, analog high resolution seismic and sidescan sonar data, seabed photographs, grain size analyses, and radiocarbon dates can be directly downloaded from NRCan’s Geogratis (http://geogratis.cgdi.gc.ca) servers. KML files allow the user to discover and explore these collections, highlighting the building blocks of marine data downloadable from ftp/http servers.SOMMAIRELa Commission géologique du Canada a mis en ligne la plupart de ses fonds de données géoscientifiques marines et y donne accès sans restriction. Ces fonds constituent des collections de premier choix de données géologiques et géophysiques de base des eaux canadiennes ou proximales. On peut ainsi télécharger des serveurs Géogratis de RNCan (http://geogratis.cgdi.gc.ca) des données d’imagerie bathymétrique par secteurs, des données analogiques séismiques haute résolution et de levé de sonar à balayage latéral, des photographies du fond marin, des analyses granulométriques, et des datations au radiocarbone. Le format KML des fichiers permet aux utilisateurs d’exploiter facilement le contenu de ces collections, en mettant en relief les données marines de base téléchargeables depuis les serveurs FTP/HTTP.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.015 |
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".