Introduction: The 11th International Circumpolar Remote Sensing Symposium
Bibliographic record
Abstract
The 11th International Circumpolar Remote Sensing Symposium (ICRSS) was held at the Scott Polar Research Institute in Cambridge from 20 to 24 September 2010. The ICRSS series began in Yellowknife in 1990 and has been held biennially since then. The 2010 meeting was the sixth time it had been held in Europe and the second time in the UK, but the first time in Cambridge. 35 people attended the meeting, from 11 countries, and over 20 oral presentations were made in addition to a well-attended poster session. The majority of the oral presentations have been developed into papers and appear in this issue of Polar Record, having been subjected to the normal peer review and editorial process, and they give a fair idea of the range of topics covered at this lively meeting. Sessions at the symposium were organised around the themes of cross-platform observations, ice and snow, topography, vegetation and observations of animals. The last theme produced three fascinating presentations on the monitoring of penguins, seals and fish from spaceborne and airborne platforms. The papers in this issue address two broad areas: terrestrial ice and snow, and high-latitude vegetation (sea ice, and polar zoology, were also represented at the meeting). All of them deal to a greater or lesser extent with technological innovation in assessing, mapping and monitoring these aspects of the polar regions, and several of them focus strongly on the development of new methods, or the assessment of newly available datasets. This issue of Polar Record thus provides a limited snapshot of the ‘state of the art’ in remote sensing of polar regions. It is the result of sustained effort by the authors of the papers, and the team of anonymous reviewers. I am glad here to record my gratitude to all of them, and to the helpers at the symposium, particularly Katya Shipigina, Allen Pope and Claire Lampitt.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.110 | 0.077 |
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".