Field data on sea ice restoration by artificial flooding in subarctic Canada
Bibliographic record
Abstract
A field campaign in the Milan Arm of Pistolet Bay in Newfoundland, Canada was conducted to gather data on sea ice restoration by artificial flooding between February and May of 2025. Sea ice thickening was initiated by pumping sea water from below the first-year sea ice onto the surface without significantly modifying the overall snow cover beforehand. Pumping consisted of 84 discrete events, for which GPS location, pumping start time and duration, and local snow and ice thicknesses were recorded. Remote data collection and monitoring were executed by three thermistor chains, three radiation sensors, and one anemometer. All remote measurement systems remained in the field until recovery of the floating systems following ice breakup in late spring. Additionally, coring systems were used to extract 10 ice cores for analysis of temperature and bulk salinity profiles through the ice depth to assess the effect of artificial flooding on sea ice formation and ablation. Two of the ice cores were used and three seawater samples were collected for analysis of the biological content of phytoplankton. Transects of surface composition across select flooded sites were assessed for the formation and solidification of ice and slush layers. Snow thickness and density data were sampled for a representative region of the entire site to assess spatial variability. All these data were complemented by timelapse camera imaging from each monitoring station and aerial drone imaging, including thermal imaging, of the entire region. The dataset can be used to investigate the physical processes involved in sea ice growth before, during, and after flooding. The dataset can be used, in a limited manner, to understand the formation, growth, and ablation of snow ice. The radiation data can be used to analyze the surface radiation fluxes of the parent, flooded, and melting ice. The data gathered during the melting season can be used to investigate the melting of thickened sea ice in comparison to that of natural sea ice. The data on bulk salinity can be used to investigate short-term brine migration. The data on phytoplankton content can be used to assess its change due to the impact of flooding. Combining the various data, thermodynamic ice growth and melt models of sea ice, including snow, slush, and snow ice, can be validated. The understanding of rain and meltwater drainage events could be improved and flow models for simulation of artificial flooding of snow-covered first-year sea ice could be further developed using the data. Aerial imagery obtained by drone provides insights into the flooding behavior of water over snow-covered ice, allowing for the detection and temporal tracking of both visibly impacted and visually concealed areas that may not be apparent to the naked eye.
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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 | 0.001 | 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 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".