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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".