Moss Competition Dynamics and Suppression Technologies in Wild Blueberry Production
Bibliographic record
Abstract
Increasing moss pressures have been observed in wild blueberry fields, which have been attributed to increased precipitation, reliance of fertilizers to provide adequate yield potential, and increasing soil organic matter content. This has coincided with uncertainty regarding the distribution and diversity of mosses present in fields, the competitive nature of selected mosses, and if required, effective suppression technologies. Research conducted from 2006 to 2009 indicated that mosses were present in every sampled field (n = 40), hair cap moss (Polytrichum commune) was the most prevalent moss species observed, and there was no apparent influence of blueberry canopy cover, soil texture, soil pH, or soil moisture on moss incidence. In addition, hair cap moss was observed to physically compete with the wild blueberry for space and remain actively growing late into the autumn and winter. Autumn applications of the herbicide Chateau® (a.i., flumioxazin) were observed to be very effective in suppressing hair cap moss pressures with minimal damage to the wild blueberry, and rapid wild blueberry growth occurred the following growing season.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".