Forest Ecosystem Classification in Manitoba : an analysis of a GIS alternative
Bibliographic record
Abstract
The purpose of this study was to evaluate the systems available in Manitoba for forest inventory and ecosystem classification and to investigate the viability of linking the Manitoba Forest Resource Inventory (FRI) with the Forest Ecosystem Classification (FEC) for Manitoba using a GIS algorithm. The algorithm used information from the FRI to reinterpret an FEC vegetation type for each of the FRI tree stands (or polygons). The FRI is the standard forest inventory tool that is widely used in Manitoba, but primarily contains information that is useful to the forestry industry (such as timber volumes). The FEC is a classification system that contains more comprehensive information regarding the forest ecosystem but has not been mapped across Manitoba. The algorithm did link the FEC vegetation type descriptions with the FRI polygons but only a weak agreement (16.03%) existed between the vegetation types derived by the algorithm and vegetation types classified on the ground in the field study. The Common Understory Species that are listed in the FEC for each vegetation type identified in the study area were assessed for utility in classifying ecosystem types. The results indicated that Boreal forest species are common across a wide variety of forest ecosystem types in the study area and the species listed as "Common" in the FEC were not good indicators of FEC vegetation type. The main conclusion from the study was that all of the options available in Manitoba for classifying forest ecosystems, including the FEC and FRI, do not fulfill the need for a spatially-broad, comprehensive, classification of forest ecosystems at the stand level.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".