Insight into successful research impacts: An environmental scan of academic and non-governmental institutes focused on the Laurentian Great Lakes ecosystem
Bibliographic record
Abstract
Protection of water quality and ecosystem health of the Great Lakes is strongly supported by people living in their watershed. Greater scientific understanding of the Great Lakes ecosystem is recognised as a key need for designing and conducting research that will provide the best means to protect the ecosystem and evaluate environmental restoration actions. Water research centres that focus on the Great Lakes aquatic ecosystem are important features of research infrastructure in the region and provide service to environmental governance, outreach, and education. An environmental scan was conducted on 22 academic and non-governmental water research centres to understand the types of institutional governance and scope of activity that leads to successful centres. All water centres are viewed favourably at their respective institutions and in their communities and serve important science communication roles with the public. Public outreach is an important function of water centres in the Great Lakes region, and greater efforts are required for fully inclusive and participatory involvement with stakeholders and rights holders. This study will be useful for any organisation seeking to develop a new or revise an existing water centre in the Great Lakes region to serve the growing need to protect water quality and ecosystem integrity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".