How nesting support type interacts with vegetation greening and distance to nest trees of mixed-species to predict white stork nest density in a Mediterranean capital
Bibliographic record
Abstract
White Stork (WS) ( Ciconia ciconia (Linnaeus, 1758)) ranks among the most common breeding birds in many Mediterranean cities, underscoring the importance of studying nest densities, particularly when populations are increasing. In this study, conducted in Rabat (Morocco), we aimed to investigate the effects of coloniality, landscape composition, and space to identify the best predictors of variation in the number of WS nests per nest support using generalized linear mixed models. The results revealed significant interactions between the type of support (trees vs. pylons) and normalized difference vegetation index (NDVI) as well as between the support type and the distance to the nearest support occupied by WS and Cattle Egret (CE) ( Bubulcus ibis (Linnaeus, 1758)) nests. A high number of nests are associated with an NDVI increase around pylons, while such an effect is insignificant around trees. In contrast, a high number of WS nests are noted close to supports occupied by both WS and CE nests, whereas in pylons, this number is recorded far away from them. The implementation of a scientific monitoring system is crucial for determining, at a defined time step, the direction and strength of relations between WS and CE populations in Rabat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".