Bibliographic record
Abstract
The coastal zone is recognized as a particularly sensitive environment to projected future climate change due to global warming. This includes sensitivity to increases in air, sea and ground temperatures; variations in the frequency of and intensity of storms; variations in sea and lake levels; variations in amounts, patterns, and styles of precipitation; and changes in sea ice extent, duration, and thickness; these changes are likely to affect coastal structures and a wide-variety of human activities. The special sensitivity of the coastal zone to climate change impacts has prompted the Government of Canada to establish a “Coastal Node ” as part of the Canadian Climate Impact and Adaptation Network (C-CIARN). A workshop with a broad representation of stakeholders from all coastal regions of Canada was held in Dartmouth, Nova Scotia in March 2001 to outline the role of a “Coastal Node”, identify a range of sensitive coastal resources and associated climate change issues, and provide guidelines for research priorities; this report summarizes the results of that workshop. C-CIARN The discussion of the C-CIARN Coastal Node was predicated on the understanding that Natural Resources Canada (NRCan) would provide some funding for a Coastal Node Coordinator, who
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.309 | 0.156 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".