EXPENDITURES General Fund Expenditures 0.8 % Page 13 NON-GENERAL FUND REVENUES
Bibliographic record
Abstract
Key to revenue trend indicators: ◄NEUTRAL ► = Variance of-1 % to 2 % compared to projections. ▲POSITIVE ▲ = Positive variance of>2 % compared to projections. ●WARNING ● = Negative variance of-1 % to-4 % compared to projections. ▼NEGATIVE ▼ = Negative variance of>-4 % compared to projections. 1 First Quarter 2011- May 2011CITY FINANCIAL OVERVIEW EXECUTIVE SUMMARY First Quarter General Fund revenues of $3,636,624 were $65,274 or 1.8 % ahead of first quarter projected revenue of $3,571,350. Property tax, natural gas utility tax, Seattle City Light contract payment, and Park and Recreation finished first quarter above projected revenue. General Fund expenditures during the first quarter of $4,128,112 were $33,789 or 0.8 % ahead of projected expenditures of $4,094,323. Street Fund revenues totaled $600,216 and were $17,375 or 3.0 % ahead of projected first quarter revenues. First quarter Street Fund expenditures totaled $527,428 which was $26,688 or 4.8 % below projections. The Surface Water Utility Fund (SWM) revenue collections during the first quarter equaled $90,707 and were $33,144 or 57.6 % higher than projections. Expenditures were $387,291 which is $153,457 or 28.4 % less than projected. Real estate excise collections totaled $178,822 which was $7,996 or 4.3 % below projections. Fuel tax collections totaled $271,682 which was $388 or 0.1 % below projections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.258 | 0.207 |
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".