Saroma-ko Lagoon Observations for sea ice Physico-chemistry and Ecosystems 2019 (SLOPE2019)
Bibliographic record
Abstract
Saroma-ko Lagoon, located on the Okhotsk Sea coast of Hokkaido, is seasonally covered by flat, homogeneous, easily accessible and safe sea ice. As such, it proves a very useful experimental site for the study of sea ice processes, the inter-comparison of methods, the testing of equipment, and the training of researchers new to the Polar regions. In this contribution, we describe a physical, chemical, and ecosystem survey at Saroma-ko Lagoon, conducted over February 23-28, 2019 under the auspices of the SLOPE2019 (Saroma-ko Lagoon Observations for sea ice Physico-chemistry and Ecosystems 2019) program. Sea ice cores were collected to examine temperature, salinity, oxygen isotopic ratio, thin sections, and chemical and biological parameters such as carbonate chemistry, CH4, nutrients, chlorophyll a concentrations, and ice algae community assemblage. Broadband and spectral irradiance measurements were carried out above/under the sea ice, and different sensors were inter-compared at close positions and environments. Equipment such as spectrometers, air-sea ice CO2/CH4 flux chamber, and under-ice turbulent heat flux systems were tested for future Arctic and Antarctic expeditions. Finally, an artificial pool was dug into the sea ice to understand the effect of snow particles on ice growth and to compare the gas exchange process over sea ice with an ice-free water surface. Our SLOPE2019 field campaign activities provided useful information for inter-comparison work and future sea ice research in the polar oceans.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".