Tara Polaris: Shedding light on microbial and climate feedback processes in the Arctic atmosphere
Bibliographic record
Abstract
The central Arctic is experiencing warming up to four times faster than the global average. This Arctic amplification is accompanied by large deviations in climate projections, making anticipation of high-impact, near-term regional biodiversity and climate change difficult. Several atmospheric processes contribute simultaneously to Arctic amplification and biodiversity change yet remain largely unstudied, not least because of the difficulty to access the central Arctic Ocean and conduct year-round studies. This article introduces the near- to mid-term objectives of the Tara Polar Station scoping group on “atmosphere-biosphere interactions,” with a focus on identifying and quantifying the origin and genetic composition of local and long-range transported biogenic particles that can impact biodiversity and cloud formation, the role of the stratified boundary layer on vertical fluxes of cloud seeds, bioaerosols and nutrients, and the impact of clouds on atmospheric light transmission. The Tara Polar Station is a fortified research vessel built to drift in the Arctic sea ice throughout the next 20 years in ten Tara Polaris expeditions, each lasting one and a half years. The platform allows for year-round interdisciplinary studies targeted at understanding the central Arctic Ocean ecosystem functioning, biodiversity, and climate change at the ocean-ice-atmosphere nexus. This scoping group will deploy novel and automated instruments for in situ, real-time vertical and remote sensing observations of aerosols, clouds, and radiation. The link between the biosphere and atmosphere will be investigated specifically through bio- and chemo-molecular sampling of air, clouds, ice, and water. We expect the early Tara Polaris expeditions to deliver insights that can be implemented into models for improved scenarios of Arctic change, in particular for the next few decades when we expect a regime shift in summer sea-ice presence.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".