Bibliographic record
Abstract
Once again I am totally impressed with the dedication and commitment of the Public Service employees of the City of Wyandotte as well as all of our outstanding residents. The biggest snow storm in the Pll$t 40 years did not stop the Public Service employees from doing what they do best, plowing and clearing the roads for its residents and anyone entering or leaving the city. I traveled around the city early this past Monday and was amazed as to how much of the snow had already been plowed, making it so much easier for residents to travel and my carriers to get from one place to another. As I mentioned to you in my letter last year, we cannot be successful delivering the mail if the Public Service employees do not get to the streets early and make it easier for us to find parking spots without getting stuck. The other member of this outstanding team has to be the residents of Wyandotte. Regardless of age, everyone was out early Monday, shoveling their snow, taking care of their letter carrier. I have been around the block and have worked in quite a few cities over the course of my 38 year career and I can honestly say that there is no city that compares to Wyandotte when it comes to taking care of its postal employees. Please take the time to extend my sincere appreciation to all of the Public Service employees for their efforts as well to the outstanding citizens of Wyandotte. I am very proud to represent this
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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.320 | 0.125 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".