The Experience of Using Autonomous Drifters for Studying the Ice Fields and the Ocean Upper Layer in the Arctic
Bibliographic record
Abstract
The constructional and operational features of the BTC60/GPS/ice temperature-profiling drifters, developed in Marine Hydrophysical institute RAS for investigation of polar areas, are considered in this article.The drifters operated in completely automatic mode measuring air pressure, water temperatures at 17 depths down to 60 m, ocean pressures at 20, 40 and 60 m nominal depths and current locations.Accuracies of measurements were: +/-2 hPa for air pressure, +/-0.1°C for temperatures, +/-30 hPa for ocean pressure, 60 m for locations.Iridium satellite communication system was used for data transfer.Time delay between sample and delivery to a user did not exceed 10 minutes.More than 30 thermodrifters were developed in the Beaufort Sea -Canada Basin and central Arctic for the period from September 2012 to September 2014.Total duration of drifting buoys in operation was more of 4800 days.It was accepted the data of hourly samples about variability of ice-flows and ice field as a whole movements, thermo processes within upper water layer below ice, air pressure in near surface atmosphere of the Arctic region.The article includes some results of statistical analysis of data from drifter ID247950, the 3-year trajectory of which depended on the processes of transfer and evolution of ice fields in the Beaufort Sea -Canada Basin.Over a long period of time the Arctic buoy in-situ experiments allowed resulting about capability and reasonability to create reliable, technological and low-cost buoy network on basis of BTC60/GPS/ice drifters to monitor Arctic area of the World Ocean.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".