The PLOCAN observatory: A multidisciplinary multiplatform observing system for the central-eastern Atlantic ocean
Bibliographic record
Abstract
The Oceanic Platform of the Canary Islands (PLOCAN) will provide rapid access to great depths at short distance from the shore, accelerating research and the generation of water column and deep-ocean knowledge. The mission of the multidisciplinary observatory is two-fold: the first is to contribute to the monitoring and modeling of coastal, regional and global ocean phenomena and ecosystems at increasing geographical scales, from shallow waters to great ocean depths. The second is the monitoring of the environmental impact and mitigation effort for all scientific and technical experiments, such as instrumentation testing, deep-sea operations and training activities from the platform. Finally one of the objectives is to offer a large yet continuously controlled area for testing deep observing systems, offering instrumentation benchmarking, calibration and validation services. The observatory has committed to contribute to and integrate with large observing initiatives, such as the European network and infrastructures ESONET-EMSO and ICOS, collaborate with international initiatives such as the North-East Pacific Time-Series Undersea Networked Experiments (NEPTUNE, Canada), and implement international standards and best-practices for sensor and data interoperability, in compliance with guidelines established for the implementation of a Global Earth Observing System of Systems (GEOSS).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".