Modelling the occurrence of rainbow lorikeets (<i>Trichoglossus haematodus</i>) in Melbourne
Bibliographic record
Abstract
Abstract Over the previous three decades, the rainbow lorikeet (Trichoglossus haematodus Family Psittacidae) has increased in urbanized areas of Australia. To help understand the nature of this increase, we investigated the influence of road density, tree cover and season on the occurrence of the rainbow lorikeet in the Melbourne region. Bayesian logistic regression was used to construct models to predict the occurrence of rainbow lorikeets, using Birds Australia atlas data at 207 2‐ha sites. The results demonstrate a strong relationship between tree cover and urbanization and the distribution of the species. The best model incorporated quadratic terms for road density and tree cover, and interaction terms, as well as season as a categorical variable. Probability of occurrence of rainbow lorikeets was highest at medium tree cover (40% to 70% of the site covered) and medium road density (9% to 12% of the surrounding area covered by roads). There was a close correspondence between the predictions of the model and new observations from bird surveys conducted at randomly selected field sites. The increased abundance of the species in urban areas has occurred despite a paucity of hollows that would act as suitable nesting sites, suggesting that only a small proportion of the population is breeding in these areas.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".