Bibliographic record
Abstract
The transforming growth factor (TGF)beta superfamily of secreted factors is comprised of over 30 members including Activins, Nodals, Bone Morphogenetic Proteins (BMPs), and Growth and Differentiation Factors (GDFs). Members of the family, which are found in both vertebrates and invertebrates, are ubiquitously expressed in diverse tissues and function during the earliest stages of development and throughout the lifetime of animals. Indeed, key roles in embryonic stem cell self-renewal, gastrulation, differentiation, organ morphogenesis, and adult tissue homeostasis have been delineated. Consistent with this ubiquitous activity, aberrant TGFbeta superfamily signaling is associated with a wide range of human pathologies including autoimmune, cardiovascular and fibrotic diseases, as well as cancer. TGFbeta superfamily ligands signal through cell-surface serine/threonine kinase receptors to the intracellular Smad proteins, which in turn accumulate in the nucleus to regulate gene expression. In addition to this universal cascade, Smad-independent pathways are also employed in a cell-specific manner to transduce TGFbeta signals. Ligand access to the signaling receptors is regulated by numerous secreted agonists and antagonists and by membrane-associated coreceptors that act in a context-dependent manner. Given the fundamental role of the TGFbeta superfamily in metazoans and the diversity of biological responses, it is not surprising that the signaling pathway is subject to tight and complex regulation at levels both outside and inside the cell. WIREs Dev Biol 2013, 2:47-63. doi: 10.1002/wdev.86 For further resources related to this article, please visit the WIREs website.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".