AMIGOS-3 multi-sensor stations and the climate, ice and ocean conditions at Thwaites Eastern Ice Shelf during 2020–22
Bibliographic record
Abstract
Abstract The Automated Meteorology—Ice—Geophysics Observation System 3 (AMIGOS-3) is a multi-sensor on-ice ocean mooring and weather, camera and precision GPS measurement station, controlled by a Python script. The station is designed to be deployed on floating ice in the polar regions and operate unattended for up to several years. Ocean mooring sensors (SeaBird MicroCAT and Nortek Aquadopp) record conductivity, temperature and depth (reported at 10 min intervals), and current velocity (hourly intervals). A Silixa XT fiber-optic distributed temperature sensing system provides a temperature profile time-series through the ice and ocean column with a cadence of 6 d−1 to 1 week−1 depending on available station power. A subset of the station data is telemetered by Iridium modem. Two-way communication, using both single-burst data and file transfer protocols, facilitates station data collection changes and power management. Power is supplied by solar panels and a sealed lead-acid battery system. Two AMIGOS-3 systems were installed on the Thwaites Eastern Ice Shelf in January 2020, providing data well into 2022. We discuss the components of the system and present several of the data sets, summarizing observed climate, ice and ocean conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".