Measurement report: Introduction to the HyICE-2018 campaign for measurements of ice nucleating particles in the Hyytiälä boreal forest
Bibliographic record
Abstract
Abstract. The formation of ice particles in Earth’s atmosphere strongly influences the dynamics and optical properties of clouds and their impacts on the climate system. Ice formation in clouds is often triggered heterogeneously by ice nucleating particles (INPs) that represent a very low number of particles in the atmosphere. To date, many sources of INPs, such as mineral and soil dust, have been investigated and identified in the lower latitudes. Although less is known about the sources of ice nucleation at higher latitudes, efforts have been made to identify the sources of INPs in the Arctic and boreal environments. In this study, we investigate the INP emission potential from high latitude boreal forests. We introduce the HyICE-2018 measurement campaign conducted in the boreal forest of Hyytiälä, Finland between February and June 2018. The campaign utilized the infrastructure of the SMEAR II research station with additional instrumentation for measuring INPs to quantify the concentrations and sources of INPs in the boreal environment. In this contribution, we describe the measurement infrastructure and operating procedures during HyICE-2018 and we report results from specific time periods where INP instruments were run in parallel for inter-comparison purposes. Our results show that the suite of instruments deployed during HyICE-2018 reports consistent results and therefore lays the foundation for forthcoming results to be considered holistically. In addition, we compare the INP concentration we measured to INP parameterizations, and we show a very good agreement with the Tobo et al. (2013) parameterization developed from measurements conducted in a ponderosa pine forest ecosystem in Colorado, USA.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".