Bibliographic record
Abstract
The study of genome size variation is important from a number of practical and theoretical perspectives. For example, the long-standing "C-value enigma" relating to the more than 200,000-fold range in eukaryotic genome sizes is best studied from a broad comparative standpoint. Genome size data are also required in detailed analyses of genome structure and evolution. The choice of future genome sequencing projects will be dependent on knowledge regarding the sizes of genomes to be sequenced, and so on. To date, genome size data have been acquired primarily by Feulgen microdensitometry or flow cytometry. Each has several advantages but also important limitations. In this review, we provide a practical guide to the new technique of Feulgen image analysis densitometry. The review is designed for those interested in genome size measurements but not extensively experienced in histochemistry, densitometry, or microscopy. Therefore, relevant historical and technical background information is included. For easy reference, we provide recipes for required reagents, guidelines for cell staining, and a checklist of steps for successful image analysis. We hope that the accuracy, rapidity, and cost-effectiveness of Feulgen image analysis demonstrated here will stimulate further surveys of genome sizes in a variety of taxa.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.079 |
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".