Integrated coastal zone management: bridging the land-water divide
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The coastal zone is an area of high ecological complexity and productivity given its intrinsic connectivity between habitats and processes of freshwater and marine aquatic ecosystems. It is also an area of complex anthropogenic interactions with variable social, economic, and cultural components. Furthermore, it is the zone where aquatic ecosystems are the most vulnerable to cumulative pressures caused by human activities of various types and intensity, where management lies within a complex jurisdictional backdrop. Thus, the coastal zone is a complex mosaic of variable zones of influences and ecosystem component vulnerabilities along the landwater interface. Canada is a maritime nation. It is bordered by the Pacific, Atlantic, and Arctic Oceans, it has the world’s longest coastline (at ca. 244 000 km), and also borders interior freshwater ”seas”, the Great Lakes. Eight out of our ten provinces border oceans, as do our three Territories. Given this backdrop, integrated coastal management seems a formidable challenge, but it is possible and it is critical that we do it strategically and efficiently with the best available information at present. Under the Health of the Oceans Initiative, four Centres of Expertise have been established within the Oceans Sector of the Department of Fisheries and Oceans in order to better understand and address national integrated coastal and oceans management issues. An overview of the objectives of the CoE on Coastal Management is presented with a focused update on its efforts in the development of ecosystem-based approaches, in relation to cumulative effects, and risk analysis decision-making tools.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".