Complex analysis and spectral theory : conference in celebration of Thomas Ransford's 60th birthday, Complex Analysis and Spectral Theory, May 21-25, 2018, Laval University, Québec, Canada
Bibliographic record
Abstract
C. Costara, Additive maps preserving matrices of inner local spectral radius zero at some fixed vector N. Levenberg and M. Perera, A global domination principle for $P$-pluripotential theory J. Esterle, A holomorphic functional calculus for finite families of commuting semigroups M. B. Bekker and J. A. Cima, An integral Hankel operator on $H^1(\mathbb{D})$ A. Belton, D. Guillot, A. Khare, and M. Putinar, A panorama of positivity. II: Fixed dimension A. G. O'Farrell, Boundary values of holomorphic distributions in negative Lipschitz classes K. Kellay, F. Le Manach, and M. Zarrabi, Cyclicity in Dirichlet type spaces R. Cheng, J. Mashreghi, and W. T. Ross, Inner vectors for Toeplitz operators R. Fournier and O. Roth, Jack and Julia Z. Abdelali, A. Bourhim, and M. Mabrouk, Spectrum and local spectrum preservers of skew Lie products of matrices K. Bickel and P. Gorkin, Numerical range and compressions of the shift J. E. Gale, M. M. Martinez, and P. J. Miana, On the asymptotics of $n$-times integrated semigroups W. Arendt and I. Chalendar, Powers of operators: Convergence and decomposition.
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.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".