Bibliographic record
Abstract
Open Access / Free-to-Read Publishers HomePeriodicalsJournal of Spectral ImagingTOS forumJournal SearchBooksSampling BookBookshopE-BooksICNIRS ProceedingsSurface Analysis BooksNMR PrimerMS HistoryFocused BooksA Pair of WharfedalesGoodwood Motor Racing BooksPublish with UsBooksellersProceedingsPublished Conference ProceedingsLet Us Publish Your ProceedingsResourcesAuthorsWhat is Spectral Imaging?NIR Discussion ForumNIR Software ArchiveWavenumber/Wavelength Converterimpublications.comPartnersSocialEvents DiaryBlogFollow Us Search Periodicals Search E-Books Search Other PagesSearch Options TOS forum HomeRead Articles TOS forum, Issue 1, p. 3 (2013)Editorial: Welcome to the inaugural issue of TOS forum!Kim EsbensenNo Abstract Available Full Text PDF (243 KB)doi: https://doi.org/10.1255/tosf.2Publication date: 3 November 2013Metrics(since September 2017)Downloads:432Citing this paper Plain Text Endnote Bibtex RIS About the EditorTOS forum is edited by Professor Kim H. Esbensen. Kim is research professor (Geoscience Data Analysis and Sampling) at GEUS (National Geological Surveys of Denmark and Greenland), external chemometrics professor with the ACABS research group, Aalborg University, Denmark and external professor (Process Analytical Technologies) at Telemark Institute of Technology, Norway. He was professor extraordinaire at Stellenbosch University (Institute of Wine Biotechnology) in the period 2005–2010 and presently Professeur associé, Université du Québec à Chicoutimi (2013–2016). He holds an honorary Doctorate at Lappeenranta University of Technology (LUT) in 2005. In 2009 he was inducted into the Danish Academy of Technical Sciences (ATV). Since 2001 he has devoted most of his scientific and R&D to the theme of representative sampling of heterogeneous systems and PAT, respectively.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.338 | 0.215 |
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".