Great Bear Rainforest - landscape level planning data
Bibliographic record
Abstract
This dataset was developed to analyze various forest management alternatives for the area under the 2016 Great Bear Rainforest order (GBR order). It was developed for the use with common forest management planning software, such as WOODSTOCK (Remsoft) or Forest Planning Studio Atlas (FPS-Atlas). The area under the GBR order objectives includes 5 timber supply areas (TSA): Kingcome, Mid Coast, North Coast, Strathcona, and small sections of the Pacific. Geographical data were collected from the BC government's open data program in Canada (DataBC), and the BC government’s website on Strategic Land and Resource Planning for the GBR. The geographical databases accessed from public sources included: (1) administrative boundaries (e.g., GBR boundary, tree farm licenses, Indian reserves, etc.), (2) forest inventory (e.g., BC vegetation resource inventory, depletions to year 2015, environmentally sensitive areas, roads), and (3) management guidance (e.g., reserves, wildlife habitat areas, ungulate winter range, recreation inventory, sensitive watersheds, streams, rivers, lakes, wetlands). Some of the geographical datasets were not publicly available (e.g., logging operability) and are therefore not part of this dataset. The productive forest land base (PFLB) was established after excluding the Provincial and National Parks, reserves, various timber licences (tree farm licences, woodlots, other leases), and non-forested land. Coniferous tree-leading stands dominate the PFLB, with the most common species being western and mountain hemlock (Tsuga heterophylla and Tsuga mertensiana) (46.8%), western redcedar (Thuja plicata) (32.6%) and yellow cedar (Chamaecyparis nootkatensis) (8.9%). The yield curves associated with each stand type, and spatially with each polygon, were imported from the latest Timber Supply Review (TSR) documents for the Kingcome and Mid Coast TSAs. For the North Coast and Strathcona TSAs, the stand type information in the latest TSR documents was used to develop yield curves using the Variable Density Yield Projection (VDYP) (Forest Analysis and Inventory Branch, 2009) and Table Interpolation Program for Stand Yields (TIPSY) (BC MFLNRO, 2016d) software tools. The Pacific TSA does not have a published TSR document, yet the small sections of the Pacific TSA that fall under the GBR (0.7%) are spatially adjacent to the Kingcome TSA. It was assumed that Pacific TSA had similar yields and stand types to the Kingcome TSA The dataset represents the status quo at data preparation in the area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".