MARINE GEOLOGY IN ATLANTIC CANADA - A GOVERNMENT PERSPECTIVE
Bibliographic record
Abstract
The two priorities for government marine geoscience over the next decades are: (1) seabed mapping for ocean management, including safe and sustainable use of natural resources; and (2) societal responses in the coastal zone to natural hazards, global climate change and anthropogenic pressures including environmental degradation. Meeting these priorities will require scientific study of the history of past glaciations; erosion, transport and flocculation processes of sea-floor sediments, particularly of muds; and sediment transport and deposition and their interaction with environmental quality in estuarine systems, including the role of ice and storms. Numerical models are required to predict the consequences of natural rise in sea level and human interference in coastal systems and for predictive decision making in ocean management. Threerecent revolutionary developments in technology will influence how science is done: these are the development of Global Positioning Systems (GPS), of multibeam sonar, and of digital data collection, storage and dissemination. However, other capital acquisitions and technological developments are necessary. These include new ships, expanded multibeam capability, and underwater autonomous vehicles. New photographic/video systems will provide resolution higher than that of multibeam bathymetry. In the coastal zone, remote sensing tools such as Light Detection And Ranging (Lidar) and kinematicGPS will accelerate monitoring of coastal change. Cabled seabed observatories will provide time series and real-time information on extreme events. Research boreholes are essential to understand geological framework.Les deux priorités du programme gouvernemental de géologie marine au cours des 20 prochaines années sont les suivantes : (1) cartographie des fonds marins pour la gestion des océans, et notamment l’utilisation sécuritaire et durable des ressources naturelles; et (2) les réponses sociétales, dans la zone côtière, aux risques naturels, aux changements climatiques planétaires et aux pressions anthropogéniques, notamment la dégradation de l’environnement. Pour réaliser ces priorités, il faudra procéder à des études scientifiques sur l’histoire des glaciations passées; l’érosion, le transport et la floculation des sédiments des fonds marins, en particulier des boues; le transport et le dépôt des sédiments ainsi que leurs relations avec la qualité environnementale dans les systèmes estuariens, notamment le rôle de la glace et des tempêtes. Des modèlesnumériques sont nécessaires pour prévoir les conséquences de l’élévation naturelle des niveaux marins et des interférences humaines dans les systèmes côtiers, de même que pour prendre des décisions prévisionnelles en gestion marine. Trois progrès révolutionnaires récents de la technologie vont avoir une influence sur les modes d’exécution des activités scientifiques : le développement du GPS, le développement du sonar multifaisceau ainsi que la collecte, le stockage et la diffusion des données numériques. Cependant, il faudra d’autres équipements et innovations technologiques, comme de nouveaux navires, d’autres dispositifs de sondage multifaisceau ainsi que des véhicules sous-marins autonomes. De nouveaux systèmes photographiques et vidéo offriront une résolution supérieure à celle de la bathymétrie multifaisceau. Dansla zone côtière, certains outils de télédétection (comme le Lidar) et le GPS en mode cinématique vont accélérer l’observation des variations côtières. Des observatoires de fonds marins câblés fourniront des séries chronologiques et des données en temps réel sur les événements extrêmes. Des sondages de prospection seront essentiels à la compréhension du cadre géologique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".