Using Biodiversity Indicators to Assess the Success of Forecasting Adaptive Ecosystem Management: The Newfoundland and Labrador Experience
Bibliographic record
Abstract
This paper reports on an initiative referred to as the Biodiversity Assessment Project (BAP). A suite of tools is being developed to assist forest managers in assessing the predicted future forest conditions of Newfoundland and Labrador?s forests under a variety of management scenarios. Since 1999, the Western Newfoundland Model Forest partnership has worked with the Institut Quebecois d?Amenagement de la Foret Feuillue (IQAFF) to develop a suite of strategic planning tools that assess the impact of various forest management scenarios on selected biodiversity indicators. This original approach began with Millar Western Forest Products Ltd. (MWFP) in Alberta, Canada, in cooperation with Peter Duinker, Lakehead University, and is now being modified to fit the Newfoundland and Labrador forest condition. The preliminary results show that forest management actions can have significant impact on various biodiversity indicators, depending on the selected management scenario. There are several components to BAP. The coarse filter layer examines the ecosystem diversity and landscape structure indices. The fine filter layer focuses on species-specific Habitat Suitability Models (HSMs). WNMF is also defining the natural disturbance regimes for western Newfoundland and comparing the selected biodiversity indicators between a natural forest condition and a managed forest. This future control forest will be used to set the natural range of variation on each biodiversity parameter being used for assessment. The BAP tools will also be developed to assess central Newfoundland eco-regions so they can be used throughout the province and applied to specific situations, such as fire-dominated ecosystems. The BAP will begin to be incorporated in the provincial wood supply analysis starting in 2005 as a prototype assessment tool.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".