Air-Deployed Microbuoy Measurement of Temperatures in the Marginal Ice Zone Upper Ocean during the MIZOPEX Campaign
Bibliographic record
Abstract
Abstract Air-deployed microbuoys (ADMBs) were developed as a means of measuring subsurface temperatures in the marginal ice zone (MIZ) over campaign-duration time scales to better understand how MIZ surface layer heat content accelerates melt rates at the edge of the ice pack. ADMBs are small, low-cost buoys deployable from unmanned aircraft and are capable of measuring temperatures to 0.1°C absolute accuracy at the surface, 1-m, and 2-m depth, along with GPS position. Each ADMB contains a microcontroller, GPS, 900-MHz radio, flash electrically erasable programmable read-only memory (EEPROM), battery, and a set of temperature sensors to monitor conditions for up to 10 days. A communications board on an overflying aircraft autonomously deploys each ADMB and collects data from previously deployed ADMBs for analysis. The 2013 Marginal Ice Zone Observations and Processes Experiment (MIZOPEX) campaign deployed ADMBs into the summer melt season MIZ north of Oliktok Point, Alaska, collecting over 400 h of data from two clusters of buoys during the short field campaign. Initial results indicate that SST is a good measure of upper-ocean temperature in the MIZ when conditions are well mixed, but that is often not the case. In areas with higher ice concentration, surface temperatures tend to underestimate the temperature of the subsurface, while in areas of low ice concentration, SSTs overestimate the subsurface temperature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".