Towards an integrated database on Canadian ocean resources: benefits, current states, and research gaps
Bibliographic record
Abstract
Oceanic ecosystem services support a range of human benefits, and Canada has extensive research networks producing growing data sets. We present a first effort to compile, link, and harmonize available information to provide new perspectives on the status of Canadian ocean ecosystems and corresponding research. The metadata database currently includes 1094 individual assessments and data sets from government (n = 716), nongovernment (n = 320), and academic sources (n = 58), comprising research on marine species, natural drivers and resources, human activities, ecosystem services, and governance, with data sets spanning 1979–2012 on average. Overall, research shows a strong prevalence towards single-species fishery studies, with an underrepresentation of economic and social aspects, and of the Arctic region in general. Nevertheless, the number of studies that are multispecies or ecosystem-based have increased since the 1960s. We present and discuss two illustrative case studies — marine protected area establishment in Canada and herring resource use by the Heiltsuk First Nation — highlighting the potential use of multidisciplinary data sets drawn from metadata records. Identifying knowledge gaps is key to achieving the comprehensive, accessible and interdisciplinary data sets and subsequent analyses necessary for new sustainability policies that meet both ecological and socioeconomic needs.
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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.047 | 0.101 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".