Bibliographic record
Abstract
Abstract:\nNew Brunswick growers produced cranberries on over 900 acres in 2016 and had a record harvest of 13,780 barrels last season. Growers are having difficulty navigating the low price concerns, and have begun to limit expenses and treatments as best they can. They are monitoring for pesticide application more than ever and have moved towards more effective use of irrigation in recent years. Weeds are beginning to be more problematic in fields, but this could also be from a mild winter. Other pest pressures have been low in 2017. One grower had extensive early leaf drop in the spring, but plants have recovered. Under a Growing Forward 2 program, the industry can access financial assistance as an incentive to plant higher yielding or earlier maturing varieties. The program will assist with the purchase of plants and associated movement costs. All other costs are not supported. Approximately 5 acres have been approved for planting. Another program helped support the purchase of the updated “Identification Guide for Weeds in Cranberries”, one for each farm in New Brunswick. Recent herbicide trial results have been inconsistent, mainly due to inadequate weed species and densities in trials from 2013-2016. Crop tolerance has been adequate for most herbicides tested, with improved safety from applications made before bud break.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.038 |
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".