Bibliographic record
Abstract
This paper deals with a new emerging science the "Seafloor Observatory Science".It is evolved rapidly over the last two decades by means of new projects and programmes towards the establishment of permanent underwater networks.The main on-going initiatives at global scale are presented for Canada (NEPTUNE -North East Pacific Time-series Underwater Networked Experiments), USA (OOI -Ocean Observatories Initiative), Japan (DONET -Dense Oceanfloor Network system for Earthquakes and Tsunamis), Taiwan (MACHO -Marine Cable Hosted Observatory) and Europe (through ESONET-NoE -European Seas Observatory NETwork-Network of Excellence and recently with the infrastructure project EMSO -European Multidisciplinary Seafloor Observatory).Moreover, the scientific motivations for seafloor observatories and their main architecture are discussed.Finally, the applications and opportunities of cabled observatories beyond those in ocean science research, technology and data services are outlined.It is important to recognise that the advent of cabled ocean observatories heralds in a new era of ocean exploration and interpretation, which will make profound contributions to socio-economic benefits, public policy formulation, and public education and engagement.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".