Annual variability of ice-nucleating particle concentrations at different Arctic locations
Bibliographic record
Abstract
Number concentrations of ice-nucleating particles (NINP) in the Arctic were derived from ground-based filter samples. Examined samples had been collected in Alert (Nunavut, northern Canadian archipelago on Ellesmere Island), Utqiagvik, formerly known as Barrow (Alaska), Nyalesund (Svalbard), and at the Villum Research Station (VRS; northern Greenland). For the former two stations, examined filters span a full yearly cycle. For VRS, 10 weekly samples, mostly from different months of one year, were included. Samples from Ny-Alesund were collected during the months from March until September of one year. At all four stations, highest concentrations were found in the summer months from roughly June to September. For those stations with sufficient data coverage, an annual cycle can be seen. The spectra of NINP observed at the highest temperatures, i.e., those obtained for summer months, showed the presence of INPs that nucleate ice up to-5 °C. Although the nature of these highly ice-active INPs could not be determined in this study, it often has been described in the literature that ice activity observed at such high temperatures originates from the presence of ice-active material of biogenic origin. Spectra observed at the lowest temperatures, i.e., those derived for winter months, were on the lower end of the respective values from the literature on Arctic INPs or INPs from midlatitude continental sites, to which a comparison is presented herein. An analysis concerning the origin of INPs that were ice active at high temperatures was carried out using back trajectories and satellite information. Both terrestrial locations in the Arctic and the adjacent sea were found to be possible source areas for highly active INPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".