The Deputy City Manager and Chief Financial Officer recommends that:
Bibliographic record
Abstract
The purpose se of this report is to provide Council with the City of Toronto Operating Variance for the year ended December 31, 2010 and to seek approval to allocate the 2010 year-end end operating surplus. In addition, Council's ouncil's approval is sought for amendments to the 2010 Operating Budget between Programs to ensure accurate reporting ng and financial accountability with no increase to the 2010 Approved Net Operating Budget. The he year-end operating surplus is $367.466 66 million for Tax Supported Operations and $63.990 million for Rate Supported Programs of the 2010 Council Approved Net Operating Budget. At its meeting of February 23 and 24, 2011, City Council approved the use of the 2010 preliminary operating surplus of $268.0 million as well as $7 million from the sale of air rights and dividends from Toronto Tor onto Parking Authority to fund the 2011 Operating Budget. After adjusting to reflect these decisions of Council, the remaining 2010 year-end end operating surplus is $92.466 66 million. It should be noted that the audit of the 2010 financial statements is not complete. As a result, there is a possibility that changes to the final surplus amount could occur.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.147 | 0.049 |
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".