Characterisation of woodwind instrument reed (Arundo donax L) degradation and mechanical behaviour
Bibliographic record
Abstract
In the present study, reeds and reed material Arundo donax L are evaluated in a long-term degradation study.Investigations into the macroscopic and microscopic behaviour of reeds suggests several deterioration mechanisms.Results can be used to inform manufacturers as to methods by which product quality control could be improved.The anatomical and materialThe author is very grateful to the many people that have aided and contributed to this work in some way.It is only through the fruitful interactions with colleagues, technicians and others around McGill and abroad that this thesis was possible.Firstly, I would like to give many thanks to my supervisor, Dr. Gary Scavone.He was willing to take on a new student venturing into a project that would require expertise in many areas.The success of this work rests on his guidance and support.I would also like to thank the students at CAML who have been a pleasure to work with, and for their time in discussing the many intricacies of experimental work.The Music Technology Area and CIRMMT have also provided support in this endeavour, facilitating travel that lead to meetings and collaborative work that form significant components of this thesis.Several other labs at McGill have provided the use of equipment and space without which many experiments would not have been possible.Dr.'s Francois Barthelat and Larry Lessard of the Mechanical Engineering Department provided the use of their labs and helpful discussions in various aspects of this work.Dr.Richard Chromik of the Materials Engineering Department facilitated meetings with himself and members of his lab and the use of experimental equipment.Dr. Dominique Derome at the Swiss Federal Laboratories for Materials Science (Empa) was willing to host me in her lab after meeting at MRS in Boston.Her support and interest in this study made the results more meaningful and provided an avenue for experimental work that was not initially thought possible.Without the input and help of these people this thesis would not have fulfilled its objectives.I would also like to acknowledge the various laboratory technicians that aided in experimental setup and sample preparation around McGill.The staff at the Goodman Cancer Research Centre are greatly acknowledged for their efforts in designing a protocol for sample preparation on a material they were seeing for the first time.The students of Luc Mongeau's lab in the Mechanical Engineering Department helped with surface probing techniques and imaging.Petr Fiurasek in the Department of Chemistry provided training and helpful discussions on the chemical characterisation of samples.Stepan Carl at Empa provided support while preforming experiments as a visiting researcher there.Daryl Cameron in the Music Technology Area helped with much of the machining work in this thesis and was vital in preparing a computer for data analysis.Thanks to all.vi Perhaps the most important people to which success is owed is my family.They have always provided support, despite my best efforts to become a career student.Grandparents on both sides of the family have always given me help and my gratitude cannot be overstated.The close bonds I share with all my family have given me the strength to power through this work.My only regret is that my Poppa has left us prior to the conclusion of this work.I know he would have loved to hear about all of my endeavours despite the endless "book learnin'."And of course, thank you to Kate Fisher, my wonderful partner who has been privy to all aspects of completing a dissertation.You'll always be my LF.
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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 | 0.000 | 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) | 0.002 | 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".