Use of expert knowledge to elicit population trends for the koala (<i>Phascolarctos cinereus</i>)
Bibliographic record
Abstract
Abstract Aim The koala is a widely distributed Australian marsupial with regional populations that are in rapid decline, are stable or have increased in size. This study examined whether it is possible to use expert elicitation to estimate abundance and trends of populations of this species. Diverse opinions exist about estimates of abundance and, consequently, the status of populations. Location Eastern and south‐eastern Australia Methods Using a structured, four‐step question format, a panel of 15 experts estimated population sizes of koalas and changes in those sizes for bioregions within four states. They provided their lowest plausible estimate, highest plausible estimate, best estimate and their degree of confidence that the true values were contained within these upper and lower estimates. We derived estimates of the mean population size of koalas and associated uncertainties for each bioregion and state. Results On the basis of estimates of mean population sizes for each bioregion and state, we estimated that the total number of koalas for Australia is 329,000 (range 144,000–605,000) with an estimated average decline of 24% over the past three generations and the next three generations. Estimated percentage of loss in Queensland, New South Wales, Victoria and South Australia was 53%, 26%, 14% and 3%, respectively. Main conclusions It was not necessary to achieve high levels of certainty or consensus among experts before making informed estimates. A quantitative, scientific method for deriving estimates of koala populations and trends was possible, in the absence of empirical data on abundances.
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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 | 0.042 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".