Modelling the occurrence of rainbow lorikeets (<i>Trichoglossus haematodus</i>) in Melbourne
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 it