Bibliographic record
Abstract
MAke dOgFighting A FelOny in All 50 stAtes by passing new laws in Idaho and Wyoming.Upgrade animal fighting laws in 15 others.pAss A lAndMArk bAllOt initiAtive in cAliFOrniA to phase out pig gestation crates, veal crates, and hen battery cages; ban veal and gestation crates in Colorado.dOUble the cUrrent list OF sOMe 400 restAUrAnt chAins, FOOd service prOviders, And edUcAtiOnAl institUtiOns who have halted or minimized the use of eggs from battery cages.Add 25 more fashion retailers and designers to the 100 who have quit using fur and fur trim.pAss MeAningFUl FederAl legislAtiOn in cOngress including the Captive Primate Safety Act, the Bear Protection Act, the Dog and Cat Fur Prohibition Enforcement Act, and a ban on imports from foreign puppy mills.stOp the cOMMerciAl seAl hUnt in Canada.clOse the lOOphOles in FederAl lAws that allow some downed cattle to enter the food supply and permit sport-hunted polar bear trophies to be imported into the U.S. increAse OUr AniMAl sAnctUAries FrOM three tO Five, establishingThe HSUS as the nation's leading provider of direct care to animals.cOMplete OUr "AFter kAtrinA" prOject to help some 60 shelters in Louisiana and Mississippi provide greater care for dogs and cats, to encourage more people to spay and neuter their animals, and to adopt pets from shelters.lAUnch A hUMAne ventUre cApitAl FUnd to invest in businesses that help animals and encourage the marketplace to offer more humane options.increAse MediA cOverAge OF AniMAl prOtectiOn issUes to raise awareness of our core issues and to stimulate public action and support.
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 | 0.003 | 0.007 |
| 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 | 0.014 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.361 | 0.404 |
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".