Bibliographic record
Abstract
information. The Board of Equalization (BOE) completed this survey in fulfillment of the provisions of sections 15640-15646 of the Government Code. These code sections provide that the BOE shall make surveys in each county and city and county to determine that the practices and procedures used by the county assessor in the valuation of properties are in conformity with all provisions of law. The Honorable David Cogdill, Stanislaus County Assessor, was provided a draft of this report and given an opportunity to file a written response to the findings and recommendations contained therein. The report, including the assessor's response, constitutes the final survey report, which is distributed to the Governor, the Attorney General, and the State Legislature; and to the Stanislaus County Board of Supervisors, Grand Jury, and Assessment Appeals Board. Fieldwork for this survey was performed by the BOE's County-Assessed Properties Division from July through August 2009. The report does not reflect changes implemented by the assessor after the fieldwork was completed. The retired assessor, Mr. Doug Harms, and the current assessor, Mr. Cogdill, and his staff gave their complete cooperation during the survey. We gratefully acknowledge their patience and
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.305 | 0.086 |
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".