Bark Splitting and Other Cold Hardiness Effects of Ornamental Herbicides
Bibliographic record
Abstract
Many nurseries within Ohio and northeastern, southeastern, and western United States, and Canada have reported severe bark splitting and scald-type problems in 2005. The amount and severity of damage seen in 2005 has been unlike anything seen before. At Ohio State University, samples from across the state started appearing in 2003–04 and increased in incidence in 2005. Growers' reports of exceeding losses of 5% of their inventory or 3000 to 4000 trees per nursery are not uncommon. At an average cost of $125 per tree and with the number of nurseries reporting problems, the stock losses in Ohio have been staggering, in excess of several million dollars. The trees that we have seen problems on in 2005 have been callery pears, yoshino cherry, kwanzan cherry, crab apples, sycamore, serviceberry, hawthorn, mountain ash, black gum, paper bark maple, japanese maples, norway maple `Emerald Queen', red maples, kousa dogwood, magnolia `Elizabeth' and the yellow magnolias such as `Butterflies', `Sawada's Cream', `Yellow Bird', and `Yellow Lantern'. It has long been observed that the actual cause of a bark crack was “preset” by a wound such as the improper removal of a basal sprout, herbicide, leaving of a branch stub, or lack of cold hardiness. Cold and frost may be contributing to the increase in bark splitting across the United States; however, new research results at Ohio State University regarding the effects of DNA preemergent herbicides in the reduction of root hardiness and regrowth potential, sprout removal and other mechanical injuries, and postemergent herbicide application will reveal these are more the causal agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".