<scp>AI</scp> sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations
Bibliographic record
Abstract
Abstract Every autumn on the south coast of Victoria Island (Nunavut, Canada), endangered Dolphin and Union (DU) caribou ( Rangifer tarandus groenlandicus x pearyi ) wait for sea ice to form before continuing their southwards migration to the mainland. Delayed freeze‐up, less stable ice conditions and ice‐breaking by vessels are putting migrating caribou at risk, but unpredictable freeze‐up times pose challenges for conservation planning. Having early warning of when the caribou sea ice crossing is likely to take place could guide more targeted measures (e.g., ice‐breaking vessel management). In this case study, we use a multi‐stakeholder approach to explore the potential of using observed and forecast sea ice concentration (SIC) to predict when DU caribou are likely to cross the sea ice. We examine links between caribou movement records and coincident satellite observations of SIC collected between 1996–2005 and 2015–2019. We establish probabilistic “percent‐crossed” metrics to convert SIC freeze‐up profiles into anticipated sea ice crossing‐start date ranges and maps. Finally, we assess the potential of using IceNet, an AI‐based 25 km resolution SIC forecast model, to predict these crossing‐start ranges in 2020–2022. We identify a clear link between SIC freeze‐up profiles and crossing‐start times, with median SIC reaching 98.8% (IQR = 94.1%, 100%) when caribou start their crossings. Our percent‐crossed metrics are effective in converting SIC records into crossing‐start date maps which can guide human experts. IceNet results show promise, predicting crossing‐start ranges comparable to those observed in 2022 up to three weeks before the first observed sea ice crossing. In 2021, IceNet's predicted ranges are systematically early, but improve between three‐ to one‐week lead times. Practical implication : AI sea ice forecasts could provide early warning of DU caribou sea ice crossing times, informing mitigation of ice‐breaking vessels and providing a blueprint applicable to other ice‐dependent species. Our case study contributes practical considerations, limitations and areas for future research to drive innovation in this emerging field forward. Ultimately, forecasts could be integrated into human‐expert centred decision‐support tools, guiding dynamic conservation and management for Arctic species.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".