TAILS N-terminomic and proteomic datasets of healthy human dental pulp
Bibliographic record
Abstract
The Data described here provide the in depth proteomic assessment of the human dental pulp proteome and N-terminome (Eckhard et al., 2015) [1]. A total of 9 human dental pulps were processed and analyzed by the positional proteomics technique TAILS (Terminal Amine Isotopic Labeling of Substrates) N-terminomics. 38 liquid chromatography tandem mass spectrometry (LC-MS/MS) datasets were collected and analyzed using four database search engines in combination with statistical downstream evaluation, to yield the by far largest proteomic and N-terminomic dataset of any dental tissue to date. The raw mass spectrometry data and the corresponding metadata have been deposited in ProteomeXchange with the PXD identifier ; Supplementary Tables described in this article are available via Mendeley Data (10.17632/555j3kk4sw.1).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".