On-site calibration of instruments in the Arctic: assessment of temperature records at Climate Change Tower in Ny-Ålesund, Svalbard
Bibliographic record
Abstract
The Arctic is as a key place to perform environmental measurements given its combination of reduced human activity and increased sensitivity to climate change. The Svalbard archipelago constitutes an invaluable measurement location, due to its ease of access and the presence of the Ny-Ålesund research center. Sensors are usually not designed to sustain prolonged periods of time in demanding environments like the Arctic; therefore, chances of failures, drift, and errors are higher than elsewhere. Maintenance and calibration of these sensors must be rigorous and frequent, to avoid poor-quality data, or even their loss. Within the frame of EURAMET EMRP project “MeteoMet 2”, calibration of the temperature sensors hosted by the Climate Change Tower (CCT), a unique research facility designed to monitor lower-atmosphere profiles of several meteorological quantities, has been performed. The calibration campaign pointed out sensor errors up to 1 °C and corrected the measurements, straightening the skewed temperature profiles. Absolute calibration uncertainties have been evaluated at ∼0.2 °C, less than half of those stated by the manufacturer, while an evaluation of relative uncertainties yielded values of just few 0.01 °C. This experience stimulated the creation of an in-situ calibration facility, to the benefit of the whole scientific community based in Ny-Ålesund.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".