Identifying the weakest link: simulating adaptive management of the reintroduction of a threatened fish
Bibliographic record
Abstract
As part of an ongoing program of management for a critically endangered fish, we explored adaptive management as a method to overcome pervasive uncertainty regarding the reintroduction of trout cod (Maccullochella macquariensis Cuvier). We simulated the entire adaptive management cycle to explore the suitability of the approach for guiding threatened species management and to identify problems and barriers to "learning by doing". During the planning phase, a number of compromises were identified between specification of goals and objectives, the available management options, and current monitoring capacity. Undertaking a simulation of the implementation of alternate adaptive approaches to this reintroduction provided a number of insights into adaptive management in general. First, identifying the weak link in the process of inference emphasized the need to consider whether goals and objectives are achievable and meaningful and whether they complement monitoring and (or) any other limitations of the system. Second, in natural resource management, it is crucial to negotiate objectives in the light of what one can measure. Third, although there are lessons to be learned from each stage of the adaptive management cycle, there is value in simulating the entire adaptive management cycle, including management actions, monitoring, and the states of the system that lead to management intervention.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".