Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it