Nectar exploitation by songbirds at Mediterranean stopover sites
Bibliographic record
Abstract
Nectar exploitation by songbirds at Mediterranean stopover sites. The nectar use by songbirds in Europe is reported by many authors but several of them refer to local or occasional events on both introduced and native plants. A study carried out on Ventotene Island (Italy) shows that nectar could be an important food resource for migrants which land at stopover sites. In this study we investigated the distribution of nectar feeding behaviour at Mediterranean stopover sites in spring, checking 10 species for the presence of pollen on plumage during ringing activities carried out at 14 stopover sites placed in Spain, Italy and Greece. Moreover we investigated the possible relationship between nectar consumption by migrants and vegetation at three stopover sites, through the time budget analysis of 8 species during no-flight activities. Sylvia and Phylloscopus species were often found to use nectar, the former more frequently than the latter. However, nectar exploitation results usual only at 2 Mediterranean sites out of 14, Ventotene and Antikythira (Greece), while it seems to be common at African stopover sites. The analysis of time budget and the pattern of nectar feeding distribution at stopover sites suggests that in the Mediterranean region nectar consumption is most likely related to the youngest phases of vegetation, these possibly being richer in flowering plants potentially usable by songbirds.
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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".