Bibliographic record
Abstract
Abstract Established primarily to improve weather monitoring across the agricultural regions of the province, the Manitoba Agriculture Weather Program (MAWP) began officially in 2005 with funding from the provincial government for the establishment of a network of 28 automated weather monitoring stations. The network steadily grew to 46 stations between 2007 and 2014 as a result of partnership with local commodity and research groups. In response to the Manitoba flood of 2011, more stations were installed and the network grew to 108 weather stations in 2019. The stations are solar powered, and scheduled maintenance is conducted at each station twice per year. Weather parameters monitored include air temperature, barometric pressure, precipitation, relative humidity, soil moisture, soil temperature, solar radiation, wind speed, and wind direction using research-grade sensors. The observations are transmitted via cellular telemetry every 15 min in the spring, summer, and fall but hourly in the winter to conserve energy supply due to reduced daylight and below-freezing temperatures. The data can be viewed by the public within 1 min of data collection. The data are used to generate agronomic-related maps such as thermal unit computation of growing degree-days and corn heat units as well as disease risk maps such as Fusarium head blight. Beyond agriculture, the data have been used for aviation investigation and for undergraduate course instruction among other applications.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.088 | 0.060 |
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".