Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Preface Contributors List of Tables List of Figures ---------------------- Part I. History and Fundamentals Karl Pearson and the Chi-Squared Test / D.R. Cox Karl Pearson Chi-Square Test-The Dawn of Statistical Inference / C.R. Rao Approximate Models / P.J. Huber -------------------- Part II. Chi-Squared Test Partitioning the Pearson-Fisher Chi-Squared Goodness-of-Fit Statistic / G.D. Rayner Statistical Tests for Normal Family in Presence of Outlying Observations / A. Zerbet Chi-Squared Test for the Law of Annual Death Rates: Case with Censure for Life Insurance Files / L. Gerville-Reache ------------------------ Part III. Goodness-of-Fit Tests for Parametric Distributions Shapiro-Wilk Type Goodness-of-Fit Tests for Normality: Asymptotics Revisited / P. Kumar A Test for Exponentiality Based on Spacings for Progressively Type II Censored Data / N. Balakrishnan, H.K.T. Ng, and N. Kannan Goodness-of- Fit Statistics for the Exponential Distribution When the Data are Grouped / S. Gulati and J. Neus Characterization Theorems and Goodness-of-Fit Tests / C.E. Marchetti and G.S. Mudholkar Goodness-of-Fit Tests Based on Record Data and Generalized Ranked Set Data / B.C. Arnold, R.J. Beaver, E. Castillo, and J.M. Sarabia ------------------------------- Part IV. Regression and Goodness-of-Fit Tests Gibbs Regression and a Test of Goodness-of-Fit / L. Seymour A CLT for the L_2 Norm of the Regression Estimators Under alpha-Mixing: Application to G-O-F Tests / C.A.T. Diack Testing theGoodness-of-Fit of a Linear Model in Nonparametric Regression / Z. Mohdeb and A. Mokkadem A New Test of Linear Hypothesis in Regression / Y. Baraud, S. Huet, and B. Laurent ------------------------------------- Part V. Goodness-of-Fit Tests in Survival Analysis and Reliability Inference in Extensions of the Cox Model for Heterogeneous Populations / O. Pons Assumptions of a Latent Survival Model / M.-L. Ting Lee and G.A. Whitmore Goodness-of-Fit Testing of the Cox Proportional Hazards Model / K. Devarajan and N. Ebrahimi A New Family of Multivariate Distributions for Survival Data / S.T. Gross and C. Huber-Carol Discrimination Index, the Area Under to ROC Curve / B.-H. Nam and R. B. D'Agostino Goodness-of-Fit Tests for Accelerated Life Models / V. Bagdonavicius and M.S. Nikulin ------------------------------ Part VI. Graphic Methods and General Goodness-of-Fit Tests Two Nonstandard Examples of the Classical Stratification Approach to Graphically Assessing Proportionality of Hazards / N. Keiding Association in Contingency Tables, Correspondence Analysis, and (Modified) Andrews Plots / R. Khattree and D.N. Naik Orthogonal Expansions and Distinction Between Logistic and Normal / C.M. Cuadras and D. Cuadras Functional Tests of Fit / D. Bosq Quasi Most Powerful Invariant Tests of Goodness-of-Fit --------------------------------- Part VII. Model Validity in Quality of Life Test of Monotonicity for the Rasch Model J. Bretagnolle Validation of Model Assumptions in Quality of Life Measurements / A. Hamon, J.F. Dupuy, and M. Mesbah  
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it