Town of North Yarmouth, Maine Annual Report Fiscal Year 2012
Bibliographic record
Abstract
age 90. She was born and raised in North Yarmouth and led an active life here. These days people are rediscovering the value of “local”; Marion lived it all her life. She was born on June 24, 1921, at Crockett’s Corner in East North Yarmouth, a daughter of Philip E. Knight and Eva Crockett. The Knights ’ farm and lumber business were centered at the Corner (Route 9, West Pownal, and Mountfort Roads). With the neighborhood kids, Marion grew up hanging May baskets, swimming in the Royal River, and eating ice cream from Dunn’s store (Route 9 by the railroad tracks). She and her family attended the North Yarmouth Methodist Church (West Pownal and Lawrence Roads). She attended the one-room Dunn’s School (at North Road and Route 9). For high school, North Yarmouth students were “tuitioned out, ” so she had to go farther afield. She decided on North Yarmouth Academy, but it was pretty far: five miles from home. So for her freshman year, Marion boarded at NYA. But she lived at home for her next three years and often caught a ride to school with her future brother-in-law, Willis Reed. She would walk home at the end of each school day. She graduated in the Class of 1939. Marion’s long working life was also locally centered. She worked at her father’s mill, Philip E. Knight Lumber Co., nailing lettuce crates and working in the mill’s office as a bookkeeper. But she is most remembered for the
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.403 | 0.267 |
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".