Towards a sustained pan-Arctic network of Distributed Biological Observatories (DBOs) - Tracking the impact of changing environmental drivers on Arctic marine ecosystems
Bibliographic record
Abstract
For over a decade, the Distributed Biological Observatory (DBO) has served as a “detection array” for ecosystem shifts in the Pacific Arctic. This long-term, collaborative effort brings together marine scientists to document how environmental change is reshaping the Arctic Ocean. The DBO concept is now expanding into other Arctic regions—Davis Strait, Baffin Bay, the Atlantic gateway, and the East Siberian Sea. By aligning methods and sharing data, these regional efforts contribute to a broader, pan-Arctic understanding. Arctic PASSION (2022–2025) has played a key role in building this network, promoting shared priorities, harmonized sampling routines, and common indicators. These efforts aim to enhance data comparability across disciplines and regions, and to support modeling and remote sensing applications. We present recent progress, outline the future scientific directions of this growing network, and share our ambition for it to serve as a backbone for long-term Arctic ecosystem observations—supporting and aligning with broader initiatives aimed at strengthening the Arctic observing system.
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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 | 0.038 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".