Bibliographic record
Abstract
This article discusses the importance of sharing information between Canada and the United States to enhance the two countries' combined defense and security. Because the two countries share an 8,891-kilometer border and are each other's largest trading partners, any significant interruption of trade would result in major economic difficulties for both countries. Several reports in both the U.S. and Canada have emphasized the need for better coordination. Recognizing this need, the North American Aerospace Defense Command (NORAD) bilateral agreement was recently expanded to add maritime warning for North America as a new mission. NORAD's new mission is focused upon information sharing between Canada and the United States regarding potential maritime threats. Placing this responsibility upon NORAD tightens the information-sharing seam between the aerospace and maritime domains and reduces the gap that formerly existed between Canadian and American defense and security organizations. Planning efforts are underway to determine where existing organizations and structures could add synergies to each other's operations while avoiding duplication of effort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.108 | 0.020 |
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".