Canada Update - Highlights of Major Legal News and Significant Court Case from January 2006 to July 2006
Bibliographic record
Abstract
Prime Minister Harper announced a long-term agreement between Canada and the United States "that resolves the longstanding softwood lumber dispute between the two countries." 1 The basic terms agreed on in April were incorporated in an agreement initialed on July 1, 2006.2All litigation over softwood lumber will end, and there will be unrestricted trade when the price of lumber is higher than $335 per thousand board feet.When prices drop below this level, Canadian lumber exports face "a combination of export charges and/or volume limits that increase as the market price drops."' 3 Of the $5 billion Canada collected in duties for softwood lumber, $1 billion will be returned to the United States.4 The U.S. lumber companies that brought suit will get $500 million, and $450 million will go to fund assistance programs, disaster relief, and sustainable forestry.5 In addition, $50 million will go to create a binational industry council composed of representatives from the U.S. and Canadian lumber industries who will "work to strengthen and integrate the North American lumber industry."' 6 Commerce Secretary Carlos M. Gutierrez said the deal "resolve[d] concerns•Lindsey Shine is a 2007 candidate for Juris Doctor from the SMU School of Law.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".