Thoughts from Canada:Sustainable Management of the Great Resource Country
Bibliographic record
Abstract
Canada is a great resource country and now is devoting itself to become a clean resource and energy country.Its sustainable management in resource and environment deserves us to learn from,which mainly in the following areas:the powers matches responsibilities in resource utilization and environment protection;respect public opinion and establish a coordination mechanism which involves wide range of stakeholders in the legal framework.;market mechanism such as emissions trading system,ecological compensation mechanism and waste recycling deposit system etc play an important role in resource utilize and environment protect;consumer awareness of environment protection has a significant impact on producer;multinational corporations play a leading role in resource utilization and environment protection;strong sense of concern and much investment in science and technology jointly promote the sustainable management of resource and environment,and so on and so forth.These reveal that we should transform advanced and scientific ideas timely into effective policy tool to much promote the work in resource utilization and environment protect .Of course,since the economic development in China and Canada is in different stages and the industrial structure and the urbanization rate are quite different in the two countries,attention should be paid to combining China's national conditions when learn Canada’s experience.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".