ClaimsPro Offers Deepest Sympathies and Full Support to Lac Mégantic
Bibliographic record
Abstract
Last Saturday a train carrying crude oil derailed, setting off a series of explosions in the downtown area. The explosions wreaked devastation on a significant number of businesses, homes, and residents of the town. ClaimsPro offers its deepest condolences to all residents of Lac Megantic, especially to the families of those who perished in this tragedy. “We’ve made the decision to handle direct damage claims, rather than liability claims, so that we can focus our efforts and resources on helping the home and business owners in Lac Megantic and avoid any conflicts of interest” shared Suzie Godmer, ClaimsPro’s Vice President of Operations in Quebec. “We’ve mobilized our resources and have full capacity to support our clients and the residents of this town.” Godmer is coordinating ClaimsPro’s adjusting services for the catastrophe. ClaimsPro has already deployed several of its major loss adjusters from its Quebec City and Sherbrooke branches to handle large commercial and personal lines property claims that have been assigned by well-known Quebec insurers. ClaimsPro’s subrogation team and Special Risk Division (SRD) are also in place to provide support and expertise as well.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.141 | 0.015 |
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".