Bibliographic record
Abstract
Grasslands are among the richest, most biodiverse ecosystems on the planet, and they are crucial in the fight against climate change. Unfortunately, since 1970 Canada has lost more than 40 percent of its grasslands, and less than 15 percent of Saskatchewan’s grasslands exist today. The province has some of the highest CO2 and methane emissions per capita and virtually no environmental regulations. How did we allow the grasslands to become one of the most endangered ecosystems on Earth? In some sense, the story of Saskatchewan fits rather neatly into the larger story of Western Canada, where politicians often care more about extraction and growing the economy while destroying the very things the economy depends on. But that isn’t the whole story. Much like Canada’s universal health care, Saskatchewan is also the birthplace of some of the first provincial and national conservation laws, and home to an unsung and unlikely champion for the environment: a farmer with a twelfth-grade education and a really old van… In Protecting the Prairies , Andrea Olive provides a history of wildlife and land conservation in Saskatchewan told through the life story of environmentalist, naturalist, farmer, and former Minister of Environment and Resource Management Lorne Scott. This is a book that challenges and inspires us to be stewards of the environment in our own backyards and communities, and above all, to never be complacent when it comes to protecting the natural world.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.013 |
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".