Special commemorative issue marking the one-year anniversary of “Genomics: the Power and the Promise 2014” / Numéro commémoratif spécial marquant le premier anniversaire de « La génomique : le pouvoir et la promesse 2014 »
Bibliographic record
Abstract
The impacts of the environmental transformation and industrialization of the Arctic are diverse and numerous.Rapid sea-ice melt threatens the unique ecosystems of the Arctic Ocean, opening new sea lanes and access to untapped oil and gas reserves.Melting permafrost transforms the tundra and destabilizes roads, airstrips, and houses.Inuit are increasingly dependent on southern goods including tobacco and industrial food that bring cancer, diabetes, and obesity.Industrialization and modernization provide economic opportunities, but they pressure the environment, the education system, and the culture of northerners.Networks of Centres of Excellence ArcticNet aims to answer the many strategic questions raised by the on-going mutation of the Canadian Arctic.It brings together the best arctic specialists in the natural, social, and health sciences and their international collaborators to inform policies and decisions related to the North and the adaptation of its communities and industries to change.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.158 | 0.068 |
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".