Revisiting properties and concentrations of ice nucleating particles inthe sea surface microlayer and bulk seawater in the Canadian Arcticduring summer
Bibliographic record
Abstract
Abstract. Despite growing evidence that the ocean is an important source of ice nucleating particles (INPs) in the atmosphere, our understanding of the properties and concentrations of INPs in ocean surface waters remain limited. We have investigated the properties and concentrations of INPs in sea surface microlayer and bulk seawater samples collected in the Canadian Arctic during the summer of 2016. We observed that 1) INPs were ubiquitous in the microlayer and bulk waters; 2) heat and filtration treatments reduced INP activity, indicating that the INPs were likely heat-labile biological materials between 0.2 and 0.02 μm in diameter; 3) there was a strong negative correlation between salinity and freezing temperatures, possibly due to INPs associated with melting sea ice; and 4) concentrations of INPs could not be explained by satellite-derived chlorophyll a concentrations. Although the spatial patterns of INPs and salinities were similar in 2014 and 2016, we did observe some differences between the years, notably: 1) the concentrations of INPs were higher on average in 2016 compared to 2014; and 2) INP concentrations were enhanced in the microlayer compared to bulk seawater in several samples collected in 2016, which was not the case in 2014.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".