Bibliographic record
Abstract
Oro-Medonte, ON – The Township of Oro-Medonte would like to extend their thanks and appreciation to the federal government for the Joint Emergency Preparedness Program (JEPP) funding received, which provides various equipment to enhance the operation of the Emergency Operation Centre (EOC) for the Township of Oro-Medonte. The JEPP funding is provincially managed by Emergency Management Ontario (EMO) on behalf of the federal government. The funding is on a 55:45 cost sharing arrangement, with the federal government paying 45%. The amount of the federal contribution is approximately $7,740. The Township used the funding to purchase a radio repeater, with battery backup, to be utilized with the additional radio frequency that was acquired. The purchase of the telecommunications system will enable the Township to provide a reliable level of service and access in the event of a power outage and/or the Emergency Plan is implemented. The Joint Emergency Preparedness Program was established in October 1980 to enhance the national capacity to respond to all types of emergencies and to enhance the resiliency of critical infrastructure. JEPP is administered by Public Safety Canada. For more information regarding the Township of Oro-Medonte’s Emergency Response Plan, please visit
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.415 | 0.083 |
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".