A question of scale: Replication and the effective evaluation of conservation interventions
Bibliographic record
Abstract
Conservation interventions can keep critically endangered species from going extinct and stabilize threatened populations. The species-specific, case-by-case approaches and small sample sizes inherent to applied conservation measures are not well suited to scientific evaluations of outcomes. Debates about whether a method “works” become entrenched in a vote-counting framework. Furthermore, population-level replication is rare but necessary for disentangling the effects of an intervention from other drivers of population change. Turtle headstarting is a conservation tool that has attracted strong opinions but little robust data. Logistical limitations, such as those imposed by the long lives of turtles, have slowed experimental evaluation and constrained the use of replication or experimental controls. Headstarting project goals vary among projects and stakeholders, and success is not always explicitly defined. To facilitate robust evaluations, we provide direction for data collection and reporting to guide the application of conservation interventions in logistically challenging systems. We offer recommendations for standardized data collection that allow their valuable results to contribute to the development of best practices, regardless of the magnitude of the project. An evidence-based and collaborative approach will lead to improved program design and reporting, and will facilitate constructive evaluation of interventions both within and among conservation programs.
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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 | 0.817 | 0.910 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".