Bibliographic record
Abstract
High Park, Scarborough Bluffs, the Humber Valley, the Port Lands. These are among the special places of Toronto. Each is a unique ecosystem within the busy urban region. Even though Torontonians think of the city as almost entirely built up, savannah or wetlands are only a subway ride away. Special Places explores the changing ecosystems of the Toronto area over this century, looking at the environmental conditions that influence the whole region and at the surprising range of plants and animals you can still find in many of its natural spaces. Special Places explores the changing ecosystems of the Toronto area over this century, looking at the environmental conditions that influence the whole region and at the surprising range of plants and animals you can still find in many of its natural spaces. In Special Places , a group of science professionals show how actions in one location produce ripples in every direction. Changes in forest cover, for example, affect not only the organisms that live in the forest but also those that use it from time to time, such as migrating birds and those that live in watersheds fed by water husbanded by forest cover. Changes in bird populations cause changes in the populations of insects on which they feed; changes in insect populations affect the plants on which they feed; and so on. As a new millennium arrives, it is time to take stock of our effect on the world around us and to consider the consequences. Special Places assesses how we can minimize the impact of human activity on the environment and even remedy some of the harm we have already done. One way is to bridge the gap between scientists and decision makers by making the natural sciences more accessible to everyone. Special Places was written at the initiative of the Royal Canadian Institute, which is the oldest active scientific society in Canada and is dedicated to bringing the natural sciences to the public. Richly illustrated and written for a general audience, this book celebrates the glory and fragility of these interlocking ecosystems and helps us appreciate the uniqueness of the "special places."
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.806 | 0.556 |
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".