Bibliographic record
Abstract
With the advent of modern chromatographic instrumentation and improved sequencing methods that allow for isolation and characterization of minute amounts of biological material, a host of biologically active peptides are now known. More recently, the dramatic development of genetic engineering has contributed to revealing the structure of complete genes coding for known, as well as novel, biologically active peptides, in addition to a large number of other peptide sequences of unknown significance. Even though precise physiological functions could be assigned to insulin, parathyroid hormone (PTH), calcitonin, growth hormone (GH), and growth hormone releasing factor (GHRH), corticotropin (ACTH) and corticotropin releasing factor (CRF), atria1 natriuretic factor (ANF), oxytocin, and a few others, the role of more than one hundred peptides displaying various biological activities in vitro or in vivo is still awaiting definition. Moreover, recent advances in immunocytochemistry and receptor imaging have revealed the ubiquity of several among these “well-established” peptides and their corresponding receptors, for which the localization and action had been previously assigned to given target organs or tissues. Most remarkable are the peptides of the now so-called “brain-gutheart” axis, for which coexistence among themselves and/or with classical neurotransmitters such as the catecholamines in nerve fibers has been recently demonstrated ( Cuello, 1982 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".