Ossification sequence of the common tern (<i>Sterna hirundo</i>) and its implications for the interrelationships of the Lari (Aves, Charadriiformes)
Bibliographic record
Abstract
The hypotheses of relationships within Lari (gulls) are highly unstable, and depend on whether morphological or molecular data are examined. Developmental sequence data are thought to contain phylogenetic information, but have never been applied to the problem of avian systematics. In this article, we describe the ossification sequence of the Common Tern (Sterna hirundo), and compare the pattern observed to published descriptions of other Charadriiformes, specifically the Great Skua (Stercorariidae) and various species of gulls (Laridae). We use changes in ossification sequence to elucidate the relationship between these three taxa, using both qualitative and systematic approaches. The first analysis of the ossification sequence data does not support a close relationship between Stercorariidae and Laridae, as has been proposed in some morphological analyses; however it was unable to differentiate between a Laridae-Sternidae sister-group relationship or a Sternidae-Stercorariidae sister-group relationship. The second analysis was unable to differentiate between any topology, including a polytomy, for these taxa. These results highlight the potential for use of ossification sequence data in an evolutionary context but caution that analyses are highly dependent on sequence resolution and the taxonomic level of the data set.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".