Can We Use Forest Inventory Mapping as a Coarse Filter in Ecosystem Based Management in the Black Spruce Boreal Forest?
Bibliographic record
Abstract
Forest inventory mapping is used worldwide to describe forests at a large spatial scale via the delimitation of portions of the landscape that are structurally homogeneous. Consequently, there is a significant amount of descriptive forest data in forest inventory maps, particularly with the development of ecosystem classification, which represents a significant potential for use in ecosystem based management. With this study we propose to test whether forest inventory maps can be used to describe not only stand characteristics but also dynamic processes. The results indicate that stand types identifiable in forest inventory maps do not in fact represent unique developmental stages, but rather confound stands at multiple developmental stages that may be undergoing different ecological processes. The reasons for this are linked to both the interaction between succession, fire severity and paludification. Finally, some aspects of the process of forest inventory mapping itself contribute to the disjunction between forest types and forest succession. Given the low similarity between spruce mapping types and their actual description following forest inventories, it would be too ambitious to infer the dynamic aspects of spruce forest by map units.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".