Bibliographic record
Abstract
Our Goals for 2009Expand programs to help animals affected by the financial crisis, such as abandoned horses and pets surrendered to shelters because of home foreclosures.End Canada's annual harp seal hunt, the largest slaughter of marine mammals in the world.Pass federal legislation to crack down on puppy mills and enact state laws to do the same, especially in Missouri, the nation's top puppy mill state. Work to advance animal protection through our 100point "Change Agenda for Animals" submitted to the Obama administration.Launch a nationwide public service campaign to promote the adoption of dogs and cats from animal shelters and combat pet homelessness in the Gulf Coast region through low-cost spay/neuter services and a public awareness campaign.Block the launch of wolf hunting programs in the lower 48 states.Reduce the suffering of farm animals by banning tail docking and other mutilations, imposing more state bans on factory-style confinement systems, and convincing major food retailers like Wal-Mart and Costco to stop selling eggs from battery cages and other factory farm products. Keep the heat on animal fighters through law enforcement training, tip lines, and reward programs.End the use of chimpanzees in invasive research and retire all 500 federally owned chimps to sanctuaries.Halt the export of U.S. horses for slaughter in Canada and Mexico and ban the transport of horses in double-decker trailers.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.273 | 0.322 |
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".