Middlesex County retirement system : financial statements : year ended December 31, 2008-2010
Bibliographic record
Abstract
fund appropriations……………………………………… 3,520,303 --Other………………………………………………………………… 386,574 12,881 28,601 Capital assets, net of accumulated depreciation………………… 5,890,910 -76,231 Total assets……………………………………………………… 781,378,583 689,135,771 595,643,271 Liabilities Accounts payable…………………………………………………… (91,779) (51,813) (44,436) Other…………………………………………………………………… --(23,357) Total liabilities…………………………………………………… (91,779) (51,813) (67,793) Net assets available for pension benefits…………………… $ 781,286,804 $ 689,083,958 $ 595,575,478 Fund Balance Annuity Savings Fund…………………………………………………$ 348,880,771 $ 330,188,697 $ 311,392,432 Annuity Reserve Fund……………………………………………… 85,458,912 83,557,603 81,027,971 Special Military Service Credit Fund……………………………… 83,769 83,519 85,558 Pension Fund………………………………………………………… 3,050,928 10,917,653 12,795,681 Pension Reserve Fund……………………………………………… 343,812,424 264,336,486 190,273,836 Total fund balance………………………………………………$ 781,286,804 $ 689,083,958 $ 595,575,478 See notes to financial statements.
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 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.068 |
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".