An option contract framework for an economy based grid resource management system
Bibliographic record
Abstract
vlll List of Tables Pa.r'ameters used for Simulating Resource Tlading for Immediate Use druing Periods of High Demand but Limited Supply of Resoulces.50 Parametels used for Simulating Resoulce Trading for Advanced Re- soulce Reservation during Periods of High Demand but Limited Sup- ply of Resoulces.Para.meters used for Simulating Resource Trading rvith Options during Periods of High Demand but Limited Supply of Resoulces.51 Parameters used fol Simulating Resource Ttading for Immediate use by Consumers during Periods of High Supply but Limited Demand for Resoulces.b.1 6.2 6.3 6.4 Paramete¡s used for Sirnulating Resource Trading rvith Advanced Reservation during Periods of High Supply but Lirnited Demand for Resources.Parameters rsed for Simulating Resource Tracling rvith Options dur'- ing Periods of High Supply but Limited Demand for Resources.lx tr.tr 54 7.I Price Paid by Resour-ce Consumels during three Scenarios ivith 1000 Resource Providei's.59 7.2 Price Paid by Resource Consumels during the thlee Scenar-ios rvith 2000 Resource Providels........ 60 7.3 Price Paid by Resoulce Consumels du'-ing three Scenarios with 3000 Resoulce Providels.62 7.4 Ealnings rnade by Resource Providers during three Scenarios with 1,000 Resoulce Consumers....... 65 7 .5Earnings made by Resoulce Providets during three Scena.r'iosrvith 2,000 Resource Consumers.65 7.6 Ealnings made by Resource Plovide¡s during thlee Scenarios rvith 3,000 Resource Consumers........ 68 List of Figures 2.I The Java Nlarket Architectule, adapted from [5].18 2.2 '|he Compute Power NIa.rket, adapted from [f 4].22 5.1 Architectulal Overvierv 40 5.2 Database lvlodel to suppolt Resource Trading for Immediate Re- souÌce use by consumers 42 5.3 Database lvlodel to slrpport Resource Tl'ading Involving Advance Re- sour-ce Reservation 43 5.4 Database Model to suppott Resoulce Tl'ading using Options 43 7 .1 Resource Allocation source Providers.Efficiency for the three Scenarios rvith 1000 Re- 7.2 Resource Allocation Efficiency for the three Scenarios rvith 2000 Re- soulce Providers.58 7.3 Resource Allocation EfiRciency for the three Scena.riosrvith 3000 Re- source Ploviders.61 7.4 Resource Allocation Efrciency for-the tlu'ee Scenalios with 1,000 Resource Consume¡s.64 7.5 Resoulce Allocation Efficiency fol the three Scenarios rvith 2,000 Resource Consrrmers.7.6 Resource Allocation Ðfficiency for the three Scenarios s,ith 3,000 Resour-ce Consumers.67 7.7 Resoulce Allocation Efficiency with Some Consurners Acting as Pr-oviders rvhen thele is a Limited Supply of Resources.69 7.8 Resource Allocation Efficiency rvith Some Consumers Acting as Providers rvhen thele is a Generous Supply of Resoulces.70 7.9 Incleasing Resources with 3000 Resource Proviclers.72 7.10 Increasing Resources rvith 3000 Resoulce Consumers.73 7.1f ANOVA Test Results fol Resource Allocation Efficiency ivith 1000 Resoulce Providers 74 7.12 ANOVA Test Results for Resource Allocation Efficiencv rvith 2000 Resource Pi-oviders 75 7.13 ANOVA Test Results for Resource Allocation Efficiency rvith 3000 Resource Providers 76 7.14 ANOVA Test Results for Resoulce Allocation Efficiency with 1000 Resource Consumers 77 7.15 ANOVA Test Results fo-,-Resource Allocation Efficiencv rvith 2000 R.esour-ce Consrrmers 7.16 ANOVA Test Results for Resoulce Allocation Efficiency with 3000
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".