Transdimensional Approximate Bayesian Computation for Inference on\n Invasive Species Models with Latent Variables of Unknown Dimension
Bibliographic record
Abstract
Accurate information on patterns of introduction and spread of non-native\nspecies is essential for making predictions and management decisions. In many\ncases, estimating unknown rates of introduction and spread from observed data\nrequires evaluating intractable variable-dimensional integrals. In general,\ninference on the large class of models containing latent variables of large or\nvariable dimension precludes exact sampling techniques. Approximate Bayesian\ncomputation (ABC) methods provide an alternative to exact sampling but rely on\ninefficient conditional simulation of the latent variables. To accomplish this\ntask efficiently, a new transdimensional Monte Carlo sampler is developed for\napproximate Bayesian model inference and used to estimate rates of introduction\nand spread for the non-native earthworm species Dendrobaena octaedra (Savigny)\nalong roads in the boreal forest of northern Alberta. Using low and high\nestimates of introduction and spread rates, the extent of earthworm invasions\nin northeastern Alberta was simulated to project the proportion of suitable\nhabitat invaded in the year following data collection.\n
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".