Bibliographic record
Abstract
Climate change is now the biggest concern in the Arctic and is causing unpredictable changes to the sea ice, making the life of polar bears (Ursus maritimus) more difficult. Although polar bears respond and adapt to all the changes in the environment actively and quickly, climate change still brings them some negative impacts. Historically, the earth has experienced many eras when the climate goes up and down. However, rising in temperature has been happening at a marked rate, 0.32°F per decade, while 2021 ranks as the sixth-warmest year. The consequences of global warming led to an earlier winter break-up and ice melting; those cause polar bears to lose their habitats and have less praying time. The populations of seals, which are their primary food resource, are also affected by the unusual climate. As a result, some of the polar bears are forced to turn their target from the ice into the shores and even human territories. The loss of ice also led to a further distance between pieces of ice, which forced polar bears to swim for a longer distance for migrating. Moreover, without enough energy being stored, pregnancy then becomes a hard task for female polar bears. Therefore, the size of litter has been declined while the health conditions of adult polar bears also went down. Although actions have been taken both nationally and internationally to prevent polar bears from going extinct and stop climate change, little achievement was made.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".