The FORWARD Project: Objectives, framework and initial integration into a Detailed Forest Management Plan in Alberta
Bibliographic record
Abstract
The Forest Watershed and Riparian Disturbance (FORWARD) project input into the Millar Western Forest Products Ltd. Detailed Forest Management Plan consists of three main components: 1) watershed and stream layer maps and associated datasets; 2) soil and wetland layer maps and associated datasets; and 3) a lookup table that permits planners to determine runoff coefficients (the variable selected for hydrological modelling) for functional first order watersheds, based upon various site factors and time since disturbance. The watershed and stream layer component includes a hydrological network, a Digital Elevation Model, and Strahler classified streams and watersheds for functional first and third order watersheds in the entire Millar Western Forest Management Agreement area. Relatively coarse mineral soils (which drain quickly) and wetlands (which retain water) were the key features that needed to be identified for the FORWARD modelling effort; therefore, the soil and wetland layers represent a combined soil texture and wetland coverage. The runoff coefficient lookup table integrates predictions of hydrologic impacts of harvest into planning. Key words: forest management, watershed, hydrology, stream, soils, wetlands, modelling
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".