Future forests and indicator-species population models
Bibliographic record
Abstract
In this paper, we provide an overview of a project that we initiated to explore the utility of spatially-explicit metapopulation models linked to dynamic landscape models as a way of incorporating biological indicators into sustainable forest management. We developed models for three indicator species as case studies; brown creeper (Certhis americana), redbacked vole (Clethrionomys gapperi) and red-backed salamander (Plethodon cinereus) in a northern Ontario landscape. Results from the project to date suggest that there are significant advantages to models that are spatially-explicit and dynamic in their treatment of both populations and landscapes. Dynamic landscape metapopulation (DLMP) models allow a manager to track population change through time in response to a changing landscape and a fluctuating environment. These DLMP models may be used to predict the impact of current and alternative forest management strategies on population sizes of a suite of species chosen to indicate the health of forest ecosystems. Key words: biological indicators, population viability analysis, population modeling, dynamic landscape modeling, sustainable forest management, brown creeper, red-backed salamander, red-backed vole
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".