Assessing Telomeric Phenotypes
Bibliographic record
Abstract
The concept of telomeres as being the end-part of eukaryotic chromosomes was first described by H. J. Muller and B. McClintock ( 1 , 2 ). Their pioneering work opened the path for multiple new researches and assays on a thrilling subject, with implications for various domains such as aging, replication, immortality, and cancer. Yeast has been a model of choice to study telomere length, senescence, telomerase activity, telomere cloning, and sequencing with important new techniques being discovered in this species and adapted afterward for other organisms. The main functions of telomeres include the protection of the genome from deletions, recombination, and degradation, and they are therefore essential for genome stability. Their maintenance is assured by a specific enzyme (telomerase) and it is of vital interest for the organism to maintain their length and specific structure. Multiple assays have been described to analyze telomere length and for yeast, Southern blot analysis of terminal restriction fragments (TRFs) remains one of the most popular ones to get a global picture of the state of telomeres in a given experimental setting. However, growth phenotypes (senescence) and fine-structure analyses of the chromosome terminal DNA are also becoming increasingly important. Therefore, the assays that determine those parameters are of highest interest when assessing telomeric phenotypes. 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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".