A profile of the eNuk environment and health monitoring program
Bibliographic record
Abstract
In Northern Canada, climate change is leading to major impacts on the health of people and the environment. Monitoring these impacts is often challenging, yet important to inform meaningful adaptation. The eNuk program is an Inuit-led, integrated environment and health monitoring program in Rigolet, Nunatsiavut, Labrador. The goal of this program is to collect comprehensive information to support communities, governments, and policy makers when responding to environmental and health indicators of climate change. An important part of the program is the eNuk app, a mobile-phone application. The app is a tool for community members to record and share their observations related to climate change while in their community and on the land. The data collected through the app will document the impacts of a changing climate on Inuit lives, health, and wellbeing. This data will help to inform action. For example, a user can record and share an incident of unseasonably thin sea ice or poor trail conditions. This information may help community members make decisions when planning their travel routes, increasing their safety. In the long term, the program will contribute to a baseline of environment and health data for Rigolet. It will record and preserve invaluable Inuit Knowledge which will help inform decision-making, policy, and programming. In addition, the eNuk program will support adaptation to climate change within the region and beyond, with actions grounded in Inuit values, knowledge, and science.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".