Bibliographic record
Abstract
DEPARTMENTS AND COLUMNS\nAT DEADLINE ............................................................................................ .3\nEDITORIAI.JCOMMENTARY .................................................................. 6\nCLOSE UP: THOMAS J. OLZAK ............................................................ 7\nPRO/CON ..................................................................................................... 8\nCORPORATE PROFILE: DR. SUSAN J. SMITH ............................. 9\nCORNER ON THE MARKET ................................................................. 10\nGETTING ORGANIZED ..................................... ~ .................................... 11\nCOMPUTERS/SOITWARE ..................................................................... 12\nEMPLOYER'S GROUP ............................................................................ l3\nMANAGING ............................................................................................... l4\nLAW .................................................................... ......................................... 1S\nLIST: HOSPITALS SERVING THE I.E ........................................ 26 & 30\nLIST: TOP SAVINGS AND LOANS FEDERAL SAVINGS BANKS ... 32\nLIST: LARGEST CREDIT UNIONS IN THE I.E ................................. 38\nDESERT BUSINESS JOURNAL ............................................................ .41\nLIST: I.E.'s MOTORCYCLE DEALERS ............................................... 44\nRESTAURANT REVIEW ......................................................................... 48\nMANAGER'S BOOKSHELF ................................................. , ................. 51\nRESOURCE DIRECTORY ......... , ............................................................ 56\nDUFF & PHELPS, LLC STOCK SHEET ............................................... 60\nCALENDAR ............................................................................................... 61\nEXECUTIVE TIME OUT ......................................................................... 62\nNEWS AND FEATURES\nThe Confederate Flag Comes to Fontana ..... .. ........................ ............ .... .. ... .. 6\nThe Art of Interviewing ...... .. ................................ .... ......... ............ ... ........... 17\nArrowhead Trust lncoJ1X>111ted Acquires Sanwa's Retirement Plan Business ....... ... 19\nThe Bank of Hemet Reports Financial Results for March 2000 .............. ... 20\nCommunity Bank Reports Net Income of $3.3 Million ......... ... ...... .......... .. 20\nTemecula Valley Bank Announces First Quarter Earnings for 2000 .......... 20\nSteve Davis is Named Vice President of PFF ............. ................................ 20\nCommunity Bancorp Reports Record Earnings ................... ........ .. ........ ..... 21\nNeed for Staff Still Unmet, Employers Tell Manpower .... ................ .... ... ... 23\nTemecula Today Feels the Effect of the Tightest Job Market in 35 Years .. 24\nNew Medical Test May Save Your Life!!! ............ ... .. ............................ ... .. 29\nRoaring Into the Millennium ......................... ............................ ............... ... 3 1\nKelly Scientific Resources Launches New Future Scientists Program ....... 33\nWhy Are Health Costs Rising Again? Are HMOs to Blame? ..................... 34\nYou CAN Cut Your Health Care Costs! .............. .. ........................... ........... 34\nPraise From the Heart and Inspiration for the Future ................................. 37\nThe Gift of Giving Comes From the Heart ................................................. 37\nCarolyn Tillman Named New Medi-Cal Project Manager for County Schools ......... 46
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.825 | 0.812 |
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".