Harvesting Icebergs and Space Rocks:Small Scale Resource Extraction Entrepreneurship in Areas Beyond National Jurisdictions
Bibliographic record
Abstract
New opportunities for small-scale, but potentially highly profitable, extractive industries either arise or have the potential to grow, both in the Arctic and in outer space. In the Arctic, icebergs which have broken off from Greenland’s glaciers and which have been transported south through the Davis Strait by the water currents are being harvested off the coast of Canada. The perceived purity of the water and the exotic origin make this risky endeavor commercially interesting. Iceberg harvesting has been undertaken for some time and is likely to become more important: along with increased melting of Greenland’s ice sheet and calving into icebergs, also the Arctic sea ice is melting rapidly due to climate change. Icebergs are an attraction in cruise tourism, too, but also pose threats particularly in the uninhabited sea routes where rescue preparedness is low and distances to help are long. Also Arcticrelated products and services are gaining the attention of consumers and investors from outside the region, in particular when high-priced luxury items and services are concerned. It therefore seems a fair assumption that iceberg harvesting off the coast of Canada is likely to remain relevant for the foreseeable future. Meanwhile, outer space is becoming the latest frontier for extractive industries, not only for the minerals and metals found on celestial bodies but also regarding water and materials which will be needed to give human activities there some degree of permanence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".