The cost‐effectiveness of biodiversity surveys in tropical forests
Why this work is in the frame
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Bibliographic record
Abstract
The identification of high-performance indicator taxa that combine practical feasibility and ecological value requires an understanding of the costs and benefits of surveying different taxa. We present a generic and novel framework for identifying such taxa, and illustrate our approach using a large-scale assessment of 14 different higher taxa across three forest types in the Brazilian Amazon, estimating both the standardized survey cost and the ecological and biodiversity indicator value for each taxon. Survey costs varied by three orders of magnitude, and dung beetles and birds were identified as especially suitable for evaluating and monitoring the ecological consequences of habitat change in our study region. However, an exclusive focus on such taxa occurs at the expense of understanding patterns of diversity in other groups. To improve the cost-effectiveness of biodiversity research we encourage a combination of clearer research goals and the use of an objective evidence-based approach to selecting study taxa.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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