Assessment of sources of variance and patterns of overlap in microchiropteran wing morphology in southeast Queensland, Australia
Bibliographic record
Abstract
In ecomorphological relationships, ecological similarities or overlap between species may occur with morphological similarity or overlap. Determination of morphological distinctness is thus important when relating morphology with ecology. This is the first of a series of papers investigating the ecomorphology of Microchiroptera in southeast Queensland, Australia, and in it I describe means and ranges of measurements and distinctness of wing morphology. In 21 species from this region, species means for aspect ratio (relative wing width) ranged from 4.98 to 8.25, while wing loading (mass by wing area) ranged from 4.32 to 15.9 N/m2. For these variables, each species' range (minimummaximum) overlaps that of at least one other species, with greater overlap at lower values. Morphological overlap was frequent, owing to a consistently wide range of wing dimensions within species, with greater overlap at low aspect ratios and wing loadings where species were more closely packed. For all variables, the variance arising from the method of measurement (wing extend and trace) was less than intraspecific variance, but in many cases was similar to interspecific overlap. A proportion of the range and overlap in wing-morphology variables is attributable to measurement variance. The variance in aspect ratio was lower than in wing loading at species, genus, family, and region levels. Phylogenetic constraint on aspect ratio appears to be greater than on wing loading, particularly at the family level. At family and genus levels, aspect ratio varied less than wing loading. No overlap in aspect ratio occurred at family level. I group species into morphologically distinct units and provide predictions of the flight behaviour of these.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".