From sea ice to seals: a moored marine ecosystem observatory in the Arctic
Bibliographic record
Abstract
Abstract. Although Arctic marine ecosystems are changing rapidly, year-round monitoring is currently very limited and presents multiple challenges unique to this region. The Chukchi Ecosystem Observatory (CEO) described here uses new sensor technologies to meet needs for continuous, high-resolution, and year-round observations across all levels of the ecosystem in the biologically productive and seasonally ice-covered Chukchi Sea off the northwest coast of Alaska. This mooring array records a broad suite of variables that facilitate observations, yielding better understanding of physical, chemical, and biological couplings, phenologies, and the overall state of this Arctic shelf marine ecosystem. While cold temperatures and 8 months of sea ice cover present challenging conditions for the operation of the CEO, this extreme environment also serves as a rigorous test bed for innovative ecosystem monitoring strategies. Here, we present data from the 2015–2016 CEO deployments that provide new perspectives on the seasonal evolution of sea ice, water column structure, and physical properties, annual cycles in nitrate, dissolved oxygen, phytoplankton blooms, and export, zooplankton abundance and vertical migration, the occurrence of Arctic cod, and vocalizations of marine mammals such as bearded seals. These integrated ecosystem observations are being combined with ship-based observations and modeling to produce a time series that documents biological community responses to changing seasonal sea ice and water temperatures while establishing a scientific basis for ecosystem management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".