Delivering 21st century Antarctic and Southern Ocean science
Bibliographic record
Abstract
Abstract The Antarctic Roadmap Challenges (ARC) project identified critical requirements to deliver high priority Antarctic research in the 21st century. The ARC project addressed the challenges of enabling technologies, facilitating access, providing logistics and infrastructure, and capitalizing on international co-operation. Technological requirements include: i) innovative automated in situ observing systems, sensors and interoperable platforms (including power demands), ii) realistic and holistic numerical models, iii) enhanced remote sensing and sensors, iv) expanded sample collection and retrieval technologies, and v) greater cyber-infrastructure to process ‘big data’ collection, transmission and analyses while promoting data accessibility. These technologies must be widely available, performance and reliability must be improved and technologies used elsewhere must be applied to the Antarctic. Considerable Antarctic research is field-based, making access to vital geographical targets essential. Future research will require continent- and ocean-wide environmentally responsible access to coastal and interior Antarctica and the Southern Ocean. Year-round access is indispensable. The cost of future Antarctic science is great but there are opportunities for all to participate commensurate with national resources, expertise and interests. The scope of future Antarctic research will necessitate enhanced and inventive interdisciplinary and international collaborations. The full promise of Antarctic science will only be realized if nations act together.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".