Simulation studies of liquidity needs, risks and efficiency in payment networks : Proceedings from the Bank of Finland Payment and Settlement System Seminars 2005-2006
Bibliographic record
Abstract
Chapter 1 Harry Leinonen Introduction 9 Chapter 2 Kimmo Soramäki - Walter Beyeler - Morten Bech - Rober Glass New approaches for payment system simulation research 15 Chapter 3 Fabien Renault - Jean-Baptiste Pecceu From PNS to TARGET2: the cost of FIFO in RTGS payment system 41 Chapter 4 Neville M. Arjani Examining the tradeoff between settlement delay and intraday liquidity in Canada's LVTS: a simulation approach 87 Chapter 5 Kei Imakubo - James J. McAndrews Funding levels for the new accounts in the BOJ-NET 123 Chapter 6 Ana Lasaosa - Merxe Tudela Risks and efficiency gains of a tiered structure in large-value payments: a simulation approach 149 Chapter 7 Stefan W Schmitz - Claus Puhr Risk concentration, network structure and contagion in the Austrian Real Time Interbank Settlement System 183 Chapter 8 Elisabeth Ledrut How can banks control their exposure to a failing participant? 227 Chapter 9 Darcey McVanel The impact of unanticipated defaults in Canada's Large Value Transfer System 253 Chapter 10 Matti Hellqvist - Heli Snellman Simulation of operational failures in equities settlement 283
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".