Bibliographic record
Abstract
Lake Ontario has profoundly influenced the historical evolution of North America. For centuries it has enabled and enriched the societies that crowded its edges, from fertile agricultural landscapes to energy production systems to sprawling cities. In The Lives of Lake Ontario Daniel Macfarlane details the lake’s relationship with the Indigenous nations, settler cultures, and modern countries that have occupied its shores. He examines the myriad ways Canada and the United States have used and abused this resource: through dams and canals, drinking water and sewage, trash and pollution, fish and foreign species, industry and manufacturing, urbanization and infrastructure, population growth and biodiversity loss. Serving as both bridge and buffer between the two countries, Lake Ontario came to host Canada’s largest megalopolis. Yet its transborder exploitation exacted a tremendous ecological cost, leading people to abandon the lake. Innovative regulations in the later twentieth century, such as the Great Lakes Water Quality Agreements, have partially improved Lake Ontario’s health. Despite signs that communities are re-engaging with Lake Ontario, it remains the most degraded of the Great Lakes, with new and old problems alike exacerbated by climate change. The Lives of Lake Ontario demonstrates that this lake is both remarkably resilient and uniquely vulnerable.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".