Is erythrocyte size a strategy to avoid hypoxia in Wiegmann’s Torquate Lizards (<i>Sceloporus torquatus</i>)? Field evidence
Bibliographic record
Abstract
This study examined changes in certain hematological parameters in a reptilian model naturally exposed to altitude-associated hypoxia. Four populations of the Mexican lizard Sceloporus torquatus Wiegmann, 1828 (Wiegmann’s Torquate Lizard) from different altitudes were sampled to evaluate erythrocyte count (Erc), hematocrit (Hct), mean corpuscular hemoglobin concentration (MCHC), and erythrocyte size (Ers). Blood was also assayed to determine hemoglobin ([Hb]), glucose, lactate, and electrolyte concentrations. Erc was performed using a Neubauer hemocytometer. Hct was calculated as percentage of packed cell volume by centrifuging blood samples. [Hb] was determined using a Bausch and Lomb Spectronic colorimeter. MCHC was calculated with the formula 100 × [Hb]/Hct. Ers was calculated from blood smear microphotographs analyzed with the Sigma Scan Pro software. Values of serum electrolytes (sodium (Na+), potassium (K+), and calcium (Ca2+)), pH, glucose, and lactate from blood samples were obtained through a blood electrolyte analyzer. Highland populations of S. torquatus exhibited a significant increase in Erc, Hct, Ers, and [Hb]. In contrast, MCHC showed no correlation with altitude. Additionally, significant differences in lactate, Na+, K+, and Ca2+ were observed in highland populations. In general, we found that most hematological parameters were significantly different among lizard populations from different altitudes. This is the first study to report changes in Ers in relation to altitude, which could be a physiological response to hypoxia.
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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 | 0.001 |
| 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".