Classifying forestland from model-generated tree species habitat suitability in the Western Ecoregion of Nova Scotia, Canada
Bibliographic record
Abstract
Forestland classification is central to the sustainable management of forests. In this paper, we explore the possibility of classifying forestland from species–habitat–suitability indices and a hybrid classification of modeled data. Raster-based calculations of species–habitat–suitability were derived as a function of landscape-level descriptions of incident photosynthetically active radiation (PAR), soil water content (SWC), and growing degree-days (GDD) for southwestern Nova Scotia, Canada. PAR and SWC were both generated with the LanDSET model and GDD from thermal data captured with the space-borne MODIS sensor. We compared the distribution of predicted forestland types with the natural range of target species as found in the provincial permanent sample plots (PSPs). Reasonable agreement (≥50% accuracy) existed between some forestland types (e.g., red maple – white birch – red oak and balsam fir – red maple) and PSP-based assessments of species presence–absence. Agreement was noticeably lower for other forestland types, such as sugar maple – beech – yellow birch (<50% accuracy). This discrepancy is attributed to forest-forming factors not directly addressed by the model, e.g., forest succession, stand interventions, and disturbance. Their addition in the model could change the dynamics of tree-species preference in southwest Nova Scotia and is worth examining. True model inaccuracies accounted for about 0.3%–15.0% of the total reported error.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".