A Novel and Low-Cost Sea Ice Mass Balance Buoy
Bibliographic record
Abstract
Abstract The understanding of sea ice mass balance processes requires continuous monitoring of the seasonal evolution of the ice thickness. While autonomous ice mass balance (IMB) buoys deployed over the past two decades have contributed to scientists' understanding of ice growth and decay processes, deployment has been limited, in part, by the cost of such systems. Routine, basinwide monitoring of the ice cover is realistically achievable through a network of reliable and affordable autonomous instrumentation. This paper describes the development of a novel autonomous platform and sensor that replaces the traditional thermistor strings for monitoring temperature profiles in the ice and snow using a chain of inexpensive digital temperature chip sensors linked by a single-wire data bus. By incorporating a heating element into each sensor, the instrument is capable of resolving material interfaces (e.g., air–snow and ice–ocean boundaries) even under isothermal conditions. The instrument is small, low cost, and easy to deploy. Field and laboratory tests of the sensor chain demonstrate that the technology can reliably resolve material boundaries to within a few centimeters. The discrimination between different media based on sensor thermal response is weak in some deployments and efforts to optimize the performance continue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".