Bibliographic record
Abstract
30 a.m.-Ok Mary.When and where were you born?I was born in Marysville, CA in 1923.-And how long has your family lived in the Marysville/Sutter Buttes area?My family came in the 1870s to Sutter County and settled in the Live Oak area.And my husband's family came here, to this ranch, in 1852.So they've been here a long time.-Yeah, is this your husband's famil-This is my husband's family ranch up here.-And do you know how he came, his family came about to own this ranch?Yes, they homesteaded it, or they did not pay for any of the land.Whatever they, they had come from Ohio and spent two winters in Iowa and then they came to Marysville in the late forties, 1840s, and then they came here in '52 and this land was granted in some manner and this is how they got it and they got around 1250 acres.And it was here, this ranch that I have is, was part of that parcel.-OK.So how did you meet your husband-Went to high school together.-To high school?In Li-Live Oak.-When you got married and moved onto the land here, is this the same, did they still have the original-No--acreage?No, this was a parcel of land that was left by the original owners to her daughter who was my husband's grandmother.And she was left this land in eighteen, approximately 1882.She lived on it, raised her family here, and my husband, Jim Spilman was raised here also.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.710 | 0.433 |
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".