Let’s Take an Adventure: Exploring Beginner Writing in Chinese by Non-Heritage Learner
Bibliographic record
Abstract
Although black bears (Ursus americanus) are among the most studied mammals in the world, little is known about their ecology in Newfoundland, Canada. I investigated the spatial ecology of black bears on the island, focusing on unusual movements during the denning period and their role as caribou (Rangifer tarandus) calf predators. I investigated the influence of climatic conditions (rainfall) and anthropogenic disturbance on the rate of den abandonment for black bears in Newfoundland, a population with an unusually high rate of abandonment given its northern latitude. I found no evidence that rainfall or anthropogenic disturbance played a role in den abandonment. My results may provide preliminary background rates of den abandonment for a northern and relatively remote ecosystem, with which to assess future change. I examined black bear predation of caribou neonates using long-term mortality and location data from 21 bears and 308 caribou. I investigated the influence of landscape features on calf vulnerability, evaluated if bears actively hunted calves, and assessed the impact of changes in the abundance and vulnerability of calves on the foraging strategy of bears. I found that landscape heterogeneity influenced calf vulnerability, and that bears selected areas where they were most likely to kill or encounter calves. Initially, daily kill rates varied with calf abundance in a type-I functional response, but, as calf vulnerability declined, kill rates dissociated from abundance. Bears adjusted their foraging strategy based upon the efficiency with which they could catch calves, highlighting the influence of predation phenology on predator space use. Most bear predation of calves occurs when caribou are aggregated on calving grounds. Some bears visit calving grounds (visitors), and thus have opportunities to prey on calves, whereas others do not (non-visitors). I evaluated differences in resource selection patterns between 4 visitor and 2 non-visitor populations (56 bears). Visitors showed stronger selection than non-visitors for local-scale landscape features associated with increased mortality risk for calves, but selection patterns were not entirely consistent among visitors and non-visitors. At the landscape-scale, most visitors displaying stronger selection than non-visitors for open landscape features associated with an increased probability of encountering caribou calves.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".