Understanding Earth– Ocean Processes using Real-time Data from NEPTUNE, Canada’s Widely Distributed Sensor Networks, Northeast Pacific
Bibliographic record
Abstract
After several years of planning, NEPTUNE Canada [www.neptunecanada.ca], as part of the Ocean Networks Canada Observatory, largely completed the installation of the world’s first regional cabled observatory network in 2009. The 800 km cable loop west of Vancouver Island connects five nodes in coastal, continental slope, abyssal plain and spreading-ridge environments. Abundant power and high-bandwidth communications support a network of hundreds of sensors that deliver data and imagery in real- or near real-time, and will transform our knowledge of the ocean environment and interacting processes. With the world’s oceans and climate in a state of crisis, the development of cabled observatory technologies is most timely and offers a growing data archive of unparalleled importance for new discoveries. Sommaire Apres plusieurs annees de planification, l’essentiel du premier reseau observatoire regional, NEPTUNE Canada [www.neptunecanada.ca], partie integrante du Ocean Network Observatory, a ete installe en 2009. Ses 800km de cable forment une boucle a l’ Ouest de l’ Isle de Vancouver et sont connectes a cinq noeuds situes au niveau de la zone cotiere, du talus continental, de la plaine abyssale et de la dorsale oceanique. Grace a cet acces a l’ energie et la communication a haut debit, un reseau de centaines de capteurs transmettent des donnees et images en temps reel ou quasi reel, qui transformeront nos connaissances du mileu et processus oceaniques. Alors que les oceans et le climat sont en etat de stress, le development des technologies liees aux observatoires sous marins represente une opportunite exceptionelle et un recueil de donnees sans cesse croissant et d’ un potentiel inegale pour permettre de nouvelles decouvertes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".