Bibliographic record
Abstract
Complete mapping of the genome in a number of organisms provides a challenge for experimental nephrologists to identify potential functions of a vast number of new genes in the kidney. Since knockout technologies have evolved in the early eighties the mouse has become a valuable model organism. Researchers can now artificially eliminate the expression of specific genes in a mammalian organism and examine the phenotype. New developments have emerged that allow investigators to knock out a gene specifically in the kidney. Several kidney-specific promoters provide valuable tools and bacterial artificial chromosome (BAC) based techniques like recombineering will enhance both number and accuracy of new mouse lines with spatially controlled gene expression. In addition to spatial control, tetracycline- or tamoxifen-inducible systems, provide the possibility of influencing the temporal expression pattern of a gene enabling researchers to dissect its functions in adult organisms. Knocking out a gene will continue to be the gold standard for defining the role of a specific gene whereas tissue-specific gene knockdown using RNA interference represents an alternative approach for generating lower-priced and fast loss of function models. In addition to reverse genetic approaches, forward genetic techniques like random mutagenesis in mice continue to evolve and will enhance our understanding of disease mechanisms in the kidney.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".