Instrument task-driven workflow software for cruise and maintenance operations
Bibliographic record
Abstract
The Digital Infrastructure group at Ocean Networks Canada (ONC) is in charge of the development and maintenance of the organization's Data Management and Archiving System (DMAS). The group has been successful in creating a software system that acquires data from large sensor networks, archives them and makes them available to a multidisciplinary community of scientists, the public, government and non-governmental agencies. DMAS also includes tools to manage the underwater infrastructure and the data flow. This paper describes a new arrival in the family of management tools: an instrument workflow system. This in-house software tool facilitates task management for all the network instruments affected in a given maintenance cruise or expedition. It was motivated by a need to ensure that all instruments are properly managed during a busy cruise season that requires domains of expertise throughout the organization. Building upon historical checklists and processes, it was designed by member teams of Digital Infrastructure department (Data Stewardship, Systems/Operations and Software Development) through consultation with key users within Ocean Networks Canada.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".