Arrow IFPA Series: Note 6 of 8: Criterion 2: Ecosystem productivity
Bibliographic record
Abstract
This extension note is the sixth in a series of eight that describes a set of tools and processes developed to support sustainable forest management (SFM) planning and its pilot application in the Arrow Timber Supply Area (TSA). It describes the criterion and two of the indicators selected to set thresholds and evaluate potential impacts on ecosystem productivity for an SFM scenario for the Lemon Landscape Unit. The note also summarizes the analysis results obtained when an ecosystem-based simulation model (FORECAST) was used to examine the effects of varying rotation length on measures of selected indicators of long-term ecosystem productivity for representative site types.The analysis considered changes in site index, soil organic matter (SOM), and site nitrogen (N) capital on poor-, medium-, and good-quality sites, and evaluated the utility of these measures in assessing and monitoring the effects of different intensities of stand management (as reflected in harvest rotation length) on ecosystem productivity.Shortening the rotation lengths increased losses of SOM and site N. The poor site showed smaller relative changes in both of these measures when compared to the good and medium sites. This suggests that different sustainability thresholds may be warranted for locations with different site qualities. Shortened rotation lengths had little effect on stemwood production (a proxy for site index), but the reduction in SOM and site N capital would likely translate into a decrease in ecosystem resiliency. Both SOM and N capital are important indicators for evaluating the sustainability of site productivity under alternative management practices. The model results highlight the value of using a multi-indicator approach when evaluating the sustainability of site productivity under alternative management practices.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".