Some recent advances in mathematics and statistics : proceedings of statistics 2011 Canada/IMST 2011-FIM XX, Montreal, Canada, 1-4 July 2011
Bibliographic record
Abstract
Preface Hypothesis Assessment Using the Bayes Factor and Relative Belief Ratio (Z Baskurt and M Evans) An Analysis of 1990-2011 Ontario Surface Air Temperatures (D R Brillinger) A Markov Chain Monte Carlo Sampler for Gene Genealogies Conditional on Haplotype Data (K M Burkett, B McNeney and J Graham) Jackknifing Stochastic Restricted Ridge Estimator with Heteroscedastic Errors (Y P Chaubey, M Khurana and S Chandra) Molecular Classification of Acute Leukemia (G Chen and J Wu) A Note on a Nonparametric Trend Test and Its Competitors (K Deka and B Gogoi) Local Search for Identifying Communities in Large Random Graphs (N Deo and M Vasudevan) Data Mining Approaches to Multivariate Biomarker Discovery (D M Dziuda) On Sequential Change-Point Detection Strategies (E Gombay) Multi-Item Two-Stage EOQ Model Incorporating Quantity and Freight Discounts (D Gupta, K Gandhi, S Makkar and P C Jha) An Improved Bound on Average Codeword Length of Variable Length Error Correcting Codes (R Gupta and B Sharma) Trimmed Analysis of Variance: A Robust Modification of ANOVA (G S Mudholkar, D K Srivastava, C E Marchetti and A G Mudholkar) Bayesian Predictive Inference under Sequential Sampling with Selection Bias (B Nandram and D Kim) Tests For and Against Uniform Stochastic Ordering on Multinomial Parameters Based on Φ-Divergences (J Peng) Development and Management of National Health Plans: Health Economics and Statistical Perspectives (P K Sen) A Unified Approach between Population-Averaged and Cluster-Specific Analyses for Multilevel Zero-Inflated Count Data (G Sneddon, M T Hasan and R Ma) Meta-Analysis of Binary Outcomes Data in Clinical Trials (P Subbaiah, A Mallick and T K Desai) Risk Reduction of the Supply Chain through Pooling Losses in Case of Bankruptcy of Suppliers Using the Black-Scholes-Merton Pricing Model (R Valvarde and M Talla) Random Effects Modelling of Non-Gaussian Time Series Responses with Covariates (G Yan, M T Hasan and R Ma).
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.023 |
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".