Comparative mechanisms for O2 storage and metabolism in two Florida diving birds: the anhinga (Anhinga anhinga) and the double-crested cormorant (Nannopterum auritum)
Bibliographic record
Abstract
Abstract Air-breathing vertebrates face many physiological challenges while breath-hold diving. In particular, they must endure intermittent periods of declining oxygen (O2) stores, as well as the need to rapidly replenish depleted O2 at the surface prior to their next dive. While many species show adaptive increases in the O2 storage capacity of the blood or muscles, others increase the oxidative capacity of the muscles through changes in mitochondrial arrangement, abundance, or remodeling of key metabolic pathways. Here, we assess the diving phenotypes of two sympatric diving birds: the anhinga (Anhinga anhinga) and the double-crested cormorant (Nannopterum auritum). In each, we measured blood- and muscle-O2 storage capacity, as well as phenotypic characteristics such as muscle fiber composition, capillarity, and mitochondrial arrangement and abundance in the primary flight (pectoralis) and swimming (gastrocnemius) muscles. Finally, we compared the maximal activities of 10 key enzymes in the pectoralis, gastrocnemius, and left ventricle of the heart to assess tissue level oxidative capacity and fuel use. Our results indicate that both species utilize enhanced muscle-O2 stores over blood-O2. This is most apparent in the large difference in available myoglobin in the gastrocnemius between the two species. Oxidative capacity varied significantly between the flight and swimming muscles and between the two species. However, both species showed lower oxidative capacity than expected compared to other diving birds. In particular, the anhinga exhibits a unique diving phenotype with a slightly higher reliance on glycolysis and lower aerobic ATP generation than double-crested cormorants.
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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 | 0.001 | 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".