The revegetation of drastically disturbed lands
Bibliographic record
Abstract
The objectives of this study were: (1) To identify and review the revegetation strategies and techniques currently being used by various agencies; (2) To assess the identified revegetation strategies and techniques for use in Manitoba; (3) To evaluate the chosen revegetation strategies and techniques through field trials; (4) To make recommendations for the revegetation of drastically disturbed lands based upon the review of strategies and techniques, and the results of field studies; and (5) To develop guidelines for "post-study" monitoring and analysis of field studies. Field trials were used to assess the potential of hydroseeding and mulching around established trees and shrubs in the revegetation of drastically disturbed lands within Manitoba and the Manitoba Model Forest. The materials that were evaluated included currently available hydroseeding products, specifically, a bonded fiber matrix and a wood fiber product with tackifiers. In addition, a paper mill sludge was evaluated for its potential as acomponent of a hydroseeding slurry and as a protective mulch (both as a dry mulch and a "hydromulch") for established trees and shrubs (Jackpine, 'Pinus banksiana'; White spruce, 'Picea glauca'; Buffaloberry (soap berry), ' Shepherdia argentia'; Dogwood, 'Cornus stolonifera'; Acute willow, 'Salk acutifolia'; Wild rose, 'Rosa sp '.; Hawthorn, 'Crataegus arnoldiana'). (Abstract shortened by UMI.)
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".