Bibliographic record
Abstract
Substantial challenges remain in our understanding of fibrotic lung diseases. Nowhere is this more true than in the elucidation and verification of the pathogenetic basis upon which they develop. Scientific progress, most recently in the field of experimental therapy, has relied closely on interpreting data derived from animal modeling. Such models are used to identify the cellular interactions and molecular pathways involved in lung tissue repair and fibrosis. Over the coming years, the significance of new discoveries will continue to be evaluated using the in vivo analysis of animal models substituting for patients with actual pulmonary fibrosis. The commonest strategy to induce experimental pulmonary fibrosis is by directly administering a profibrotic agent to either wild-type animals or those that bear a specific genetic modification. The creation of new models has been greatly enhanced by the availability of stem cell lines and methods for introducing genetic mutations into these cells. Despite an increasing choice of models, there are still good reasons to continue adapting and using one of its earliest examples, the bleomycin model, in post-genomic pulmonary fibrosis research. A brief review of the exacting requirements of such research will place the strengths of this particular model in perspective.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".