Bibliographic record
Abstract
The 2022 Russian invasion of Ukraine generated two simultaneous security crises for NATO: firstly, the obvious military one and, secondly, the energy crisis in Europe.In one sense, these were old issues reigniting fears of Russian intentions on the continent and reminding older residents of fuel supply crises of earlier times related to conflict in the Middle East.But in the context of rapidly advancing scholarship on how the Earth's ecology functions, and the dangerous heat wave worldwide in 2023, massive forest fires in Canada, and record highs in global sea surface temperatures, those old concerns must be supplemented by a clear appreciation of the new dangers that are looming in the immediate future: a third security crisis related to the rapid ecological transformations now underway around the world.Climate change is the most important consideration, but far from the only one as the burgeoning earth system sciences have been making clear in recent years. 1 A Canadian Armed Forces Flight Engineer surveys the wildfires in British Columbia from onboard a CH-147 Chinook helicopter during a reconnaissance flight as part of Operation LENTUS 17-04 in Kamloops British Columbia, on July 12, 2017.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.042 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 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".