Long-Term Effects of Seeding Densities and Fertilization Treatments Using Seed from Herbaceous Species Native to the Northern Interior of British Columbia
Bibliographic record
Abstract
Establishing vegetation to control erosion, rebuild the soil and improve the visual appearance of degraded sites is an important aspect of ecosystem restoration. However, the maintenance of biodiversity and ecosystem function, wildlife management and aesthetic appeal are also important factors. The use of native species for purposes of revegetation is therefore an important consideration in addressing all of these issues but there is little information regarding their use. In an attempt to fill gaps in knowledge related to the use of seed from native species, an experiment to test seeding densities, one-time fertilization application, the season of seeding and their interactions was established on six degraded sites in the northwestern interior of British Columbia. Plots were established in 1999 (fall) and 2000 (spring) and monitored for cover and emergence in 2000 and 2001. Results suggested that sowing density and fertilizer were both important considerations when attempting to revegetate degraded sites (Burton 2003). Since research related to the use of native species is limited it was felt that further monitoring of this experiment could yield important information regarding the use of native seed and the plots were subsequently monitored in 2002 and 2003. The best treatment combinations (>3000 PLS/m2, sown with fertilizer in the spring) did not change markedly over the years, though statistically equivalent results can be achieved with as little as 375 PLS/m2 if high cover is not needed in the first year. Decreases in mean cover in Years 3 and 4 have suggested that repeated fertilization or increased use of legumes in the mixture would be needed to maintain high levels of cover.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".