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
Within the growing field of proteomics, mass spectrometry is now established as a powerful tool for peptide and protein identification and discovery from purified samples. A new era is now beginning, with the development of MALDI imaging, maintaining the sensitivity and efficacy of both discovery and identification while additionally preserving the anatomical integrity of biomolecules like peptides, proteins, oligonucleotides and lipids within tissues. Crucial developments for sample preparations have made leaps and bounds, as it is now possible to work with freezed conserved biopsies (- 80 degrees c) of more than 6 months or even conserved after paraformaldehyde fixation and paraffin embedding. The latter development has opened the door to archived tissues in hospital libraries and biomarkers hunting from tissues derived from these libraries are now a key objective. The relationship between MALDI imaging and immunocytochemistry used by the pathologist is important. The development of specific MALDI imaging using probes with a tag (peptide or organic) called << Tag-Mass >> adds a whole new perspective. It is possible henceforth to localize a protein with its specific mRNA and more specifically, with its signalling pathway on the same sections or within a pathology expression phenotype from a biopsy. Development of such a technology is similar to the one that occurred several years ago for nuclear magnetic resonance (NMR) that leads the development of imaging technologies called MRI in hospital which is intensively used for pathology diagnostics.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.002 |
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