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Record W7066097941

Exterior characteristics and internal wood decay of hazard trees in Thunder Bay, Ontario / by Kurtis Barker.

2017· other· en· W7066097941 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsThunderPruningMapleHazardCrown (dentistry)Woody plantTree healthTaigaForest health
DOInot available

Abstract

fetched live from OpenAlex

One major component of urban forestry is the ability to recognize hazard trees and have them removed before they fail or cause damage to people or property. Thunder Bay's urban forest consists of approximately 20,000 street trees, many of which are at the end of their lifespan and becoming hazardous. Each year 200 or more street trees are removed by the city which provided an opportunity to undertake this research project to identify and possibly equate the relationship between exterior characteristics of trees deemed hazardous, with interior damages, i.e. wood decay. Secondary objectives of this project included measuring the occurrence and diversity of wood decay fungi on street trees in Thunder Bay, creating a resistograph measurement atlas, and determining if a relationship
\nbetween resistograph measurements and wood strength values (density and MOE) is possible.
\nIn total, exterior characteristics were measured on 177 hazard trees destined for removal, 65 in the summer of 2011 and 112 in the summer of 2012. Of the trees measured, 19 species were represented, with white birch (26%), silver maple (16%), green ash (14%), and Manitoba maple (9%)
\nbeing the most commonly occurring. White birch had the lowest health ratings, with crown die-back brought on by drought stress and bronze birch borer infestation. Damage by severe pruning on major limbs resulted in extensive decay observed among many of the white birch. Silver maple exhibited a decrease in trunk health and structure with increasing age. Large pruning wounds which facilitated invasion by decay fungi, and co-dominant forking were common problems encountered on silver maple.
\nGreen ash experienced damage to foliage and exhibited twig and branch die-back in the lower crowns of trees due to ash anthracnose.
\nAlthough discs should have been removed from all 177 street trees, only 26 tree discs from this pool of trees were collected by Parks crews. An additional 44 tree discs were collected by Parks crews that came from trees not on the original tree removal lists. Due to the small sample size for each tree species, it was not possible to find a correlation between exterior characteristics and internal defects. However, all tree discs (70) were photographed and drilled with a resistograph (IML Resistograph F-series) and are illustrated along with the resistograph charts in Appendix III. Cracks and advanced decay were readily recorded with the resistograph, but incipient decay was not detected as it had readings similar to sound wood. A significant source of error noted was drill bit deflection caused by knots in the wood, contours, and meandering growth rings.
\nOne hundred and one trees were observed to be colonized by decay fungi, 22 of these trees were among the original 177, while the remaining trees were additional street trees observed in Thunder Bay. Twenty seven species of decay fungi were recorded, and among these, Cerreno unicolor,
\nHypsizygus tessulatus, Pholiota aurivella, Chondrostereum purpureum, Pholiota squarrosa, and Ganoderma applanatum were among the most commonly encountered. Particular attention needs to be paid to silver maple and white birch which were most heavily infected. Green ash exhibited the lowest infection by decay fungi.
\nSixteen resistograph readings from two bolts from a silver maple were taken to compare the values on the y axis with actual wood density and MOE values. Results of a linear regression model suggest that a relationship exists. Although more trees would need to be tested in order to strengthen
\nthe interpretation of the relationship. This could provide for a methodology to be created which would give urban foresters measurements that could more accurately predict when a tree is likely to fail.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.241
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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