Nutrient allocation to eggs in female Argentine shortfin squid, <i>Illex argentinus</i> using fatty acids as nutrient indicator
Bibliographic record
Abstract
Fatty acids play a critical role in embryonic development of cephalopods. However, little information is available on the allocation of fatty acids to eggs during oogenesis, limiting our understanding regarding how these animals maximize reproductive performance in terms of energy and nutrient use. We explored the nutrients for egg production during maturation for Argentine shortfin squid ( Illex argentinus (Castellanos, 1960)) by comparing the fatty acid profiles between the ovary and eggs in the oviducts. We detected 30 fatty acids in the ovary and eggs, of which 19 constituted more than 0.2% of the total fatty acid content. The overall fatty acids in the ovary varied significantly among maturity stages, while the eggs had a consistent amount of total fatty acids and relative amount of individual fatty acids. There were consequently significant differences in the fatty acid profiles between the ovary and eggs by maturity stage and in total. Additionally, eggs had more saturated fatty acids but less polyunsaturated fatty acids than the ovary. Cumulatively, our results reveal that this squid produces eggs with consistent levels of nutrients virtually regardless of how the nutrient profile of the ovary varies during maturation, providing insight into the egg production process relation to nutrient allocation.
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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 | 0.000 | 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 | 0.000 | 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".