Application of flow cytometric cell cycle analysis to the assessment of condition and growth in larvae of a freshwater teleost <i>Galaxias olidus</i>
Bibliographic record
Abstract
Through its ability to measure cell DNA content, flow cytometric analysis (FCA) is a technique capable of accurately assessing the position of cells in the cell cycle. Using FCA to measure the proportion of dividing and nondividing cells, an index was created that allows the amount of cell division within larval fish tissues to be quantified. To assess the suitability of the cell division index (CDI) as an indicator of growth and condition in fish larvae, analyses were divided into four parts. These examined the effects of temperature, nutrition, time of day, and geographic location on the CDI of brain tissue from Galaxias olidus larvae. The index was sensitive to, firstly, differences in the brain CDI of larvae reared at 12 and 20°C and, secondly, to significant fluctuations in mean brain CDI from larvae sampled over 24 h. FCA also revealed significant differences in the CDI of starving and fed larvae. Overall, this study indicates that FCA may be suitable as an indicator of growth and condition in both laboratory-reared and wild fish larvae.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".