Bibliographic record
Abstract
wha he U om llut blubber. Maternal transfer of contaminants to the juveniles during rapid development of their biological systems may put these young whales at greater risk than adults for adverse health effects (e.g., immune orca) fo laska in lation gered ” in Canada in 2001 and the US in 2005. Although the population had rebounded to 91 whales by 2005, the July 2008 count was only 85 individuals (Carretta et al., 2007; Center for Whale Research, 2008). The population decrease in the late 1990s was accompanied by large differences in survival rates among age classes, sexes, and pods, suggesting external causes, such as environmental conditions (e.g., El Niño events) during mals (de Swart et al., 1994; Hall et al., 2006; Jepson et al., 2005; O’Hara and O’Shea, 2001; Ross et al., 1995). Furthermore, immune dysfunction, thyroid disruption and neurotoxicity were observed in laboratory animals exposed to PBDE congeners (de Wit, 2002; Eriksson et al., 2001, 2002). High levels of POPs (e.g., PCBs and DDTs) were found in blubber of eastern North Pacific killer whales in a few early studies (Calambokidis et al., 1984; Hayteas and Duf-
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.254 | 0.092 |
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".