Snipe taxonomy based on vocal and non‐vocal sound displays: the South American Snipe is two species
Bibliographic record
Abstract
We analysed breeding sounds of the two subspecies of South American Snipe Gallinago paraguaiae paraguaiae and Gallinago paraguaiae magellanica to determine whether they might be different species: loud vocalizations given on the ground, and the tail‐generated Winnow given in aerial display. Sounds of the two taxa differ qualitatively and quantitatively. Both taxa utter two types of ground call. In G. p. paraguaiae , the calls are bouts of identical sound elements repeated rhythmically and slowly (about five elements per second (Hz)) or rapidly (about 11 Hz). One call of G. p. magellanica is qualitatively similar to those of G. p. paraguaiae but sound elements are repeated more slowly (about 3 Hz). However, its other call type differs strikingly: it is a bout of rhythmically repeated sound couplets, each containing two kinds of sound element. The Winnow of G. p. paraguaiae is a series of sound elements that gradually increase in duration and energy; by contrast, that of G. p. magellanica has two or more kinds of sound element that roughly alternate and are repeated as sets, imparting a stuttering quality. Sounds of the related Puna Snipe ( Gallinago andina ) resemble but differ quantitatively from those of G. p. paraguaiae . Differences in breeding sounds of G. p. paraguaiae and G. p. magellanica are strong and hold throughout their geographical range. Therefore we suggest that the two taxa be considered different species: G. paraguaiae east of the Andes in much of South America except Patagonia, and G. magellanica in central and southern Chile, Argentina east of the Andes across Patagonia, and Falklands/Malvinas.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".