A synthesis of the Great Lakes Restoration Initiative according to the Open Standards for the Practice of Conservation
Bibliographic record
Abstract
The Great Lakes Restoration Initiative (GLRI), designed to restore and protect the ecology of the Laurentian Great Lakes, is one of the largest environmental funding programs in the United States. Over 5,400 grants have been awarded in the last 11 years (2010–2020), representing over $3.5 billion in federal spending. A publicly available database that contains a written description about each grant is available online. However, analysis cannot easily be performed given that the descriptions are only textual. Therefore, we applied a modified version of the Conservation Action Classification (CAC 2.0), an established framework from the Open Standards for the Practice of Conservation, to synthesize the number of restoration actions, target species, and specific threats mentioned using thematic content analysis. The framework was modified to expand the CAC 2.0 by adding actions specific to GLRI. For example, we created typologies for the monitoring performed, site stewardship actions, and maritime ballast management practices. Based on this tally, we provide a summary of all the GLRI efforts to date. In addition to the more widely known restoration actions, we also describe the extent of educational, capacity building, and the non-monetary value projects that considered human wellbeing and/or focused on traditional ecological knowledge, recreation, or public outreach and engagement. Finally, we conclude with a discussion about the state of GLRI, the extent of the social or community-oriented efforts, and possible areas for adaptive management. This systematic coding process, and our shared supplementary data, can assist future GLRI research and strategic planning.
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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 | 0.068 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".