Manitoba Metis Foundation Weather Keeper Summary French
Bibliographic record
Abstract
Water-weather keeper and water quality monitor pilot programs were developed in coordination with the MMF and were designed to build Métis capacity in water quality management activities and increase Métis ability to make science-based decisions about climate and nutrient issues in the basin. These programs will underpin a co-developed and jointly managed monitoring network that will allow for a system-level understanding of how the MBGL respond to land-use change and variability in weather, including those elements influenced by regional changes in climate. Des programmes pilotes de surveillance des conditions météorologiques et de la qualité de l’eau ont été mis sur pied en collaboration avec la MMF et conçus pour accroître la capacité métisse à se livrer à des activités de gestion de la qualité de l’eau et prendre des décisions scientifiques sur les questions de climat et de nutriments dans le bassin. Ces programmes soutiendront un réseau de surveillance créé et géré conjointement afin de mieux comprendre le système et la manière dont les MBGL répondent aux modifications d’utilisation des terres et variables météorologiques, notamment aux éléments influencés par les changements climatiques régionaux.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.202 | 0.032 |
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".