Bibliographic record
Abstract
As the Arctic is encountering many environmental changes, a multi-species meta-analysis was conducted from peer-reviewed scientific data gathered from the University of Nebraska-Lincoln (UNL) library’s bibliographic databases to determine 1) the main polar warming impacts on species of the Arctic food web such as polar bears, ringed seals, Arctic cod, copepods, and primary producers, 2) determine climatic impacts by looking at population sizes and migratory patterns over several years, and 3) discover impacts of a top-down cascade and look into conservation efforts. In the Arctic, species within its’ ecosystem are experiencing more dramatic impacts from climatic warming because of the albedo positive feedback loop the cryosphere regions are experiencing. The purpose of this analysis was to help understand the impact that climate change can have on the different parts of the Arctic ecosystem and to further understand how all the species work in an ecosystem, potentially leading to different conservation efforts better suited for species survival. The data was narrowed down to research founded in Hudson Bay, Canada, where the subpopulation of polar bears was declining in abundance. Ringed seals, polar cod, benthic organisms, and algae were all experiencing negative effects of climatic change separately, nothing linking their populations to one another. Overall, the research indicates that bottom-up and top-down trophic cascades can be assumed but each species down or up the food chain is being negatively impacted by their own global warming challenge. It is important to maintain a balance in the Arctic food web to maintain a healthy ecosystem, planning for the future and making efforts to advocate biodiversity will be the best custom in polar maintenance and increasing species survival.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".