Persistent spatial gaps in ornithological study in Australia, 1901–2011
Bibliographic record
Abstract
At the continental scale, ecological research effort is not spatially uniform. We used a century-long bibliometric database of the journal Emu – Austral Ornithology to index the spatial patterns in bird research in Australia (from articles with explicit study locations). Studies have been concentrated in Tasmania and the southwest, southeast and coastal parts of the mainland. Large spatial gaps exist in ornithological study, which are similar to those identified by Arnold Robert McGill in his 1948 review paper ( McGill 1948 ). Pre-1948 only 9.4% of articles [n = 2,107] fell within the gaps mapped by McGill in 1948, indicating that his mapping was largely accurate. These gaps have largely persisted; only 11.2% of the 1,498 articles published since 1948 came from within those gaps. We present a complementary spatial gap analysis, which focuses on studies of areas with broadly similar biogeographies (Interim Biogeographical Regions of Australia (IBRAs)). Of 85 mainland IBRAs (of 89 defined), five have no bird studies from within them (368,380 km2; 4.9% of Australia), and 34 have less than 10 studies (3,335,498 km2; 43.9%). We intersect IBRAs with McGill's gaps and show that some IBRAs within McGill's gaps are now better-studied, but 64.8% of the area within the McGill gaps boundaries comprises IBRAs where there have been no post-1948 studies in Emu. We also present an updated map of key geographical gaps in the study of Australian birds, which apparently remain extensive 60 years after they were first identified.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.038 | 0.054 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".