Bibliographic record
Abstract
The vegetation within the southwest Yukon consists of a complex mosaic of boreal forests dominated by white spruce (Picea glauca) and Festuca-Artemisia grasslands.In this study, these forest-grassland ecotones were used to study the effects of different vegetation types on a variety of soil properties including; percent moisture, fine earth bulk density, pH, total carbon and nitrogen, organic matter, total carbonate carbon and soil organic carbon.The effects of vegetation on these soil properties were able to be studied independently from other soilforming factors because the transition in vegetation occurs over relatively small spatial scales in which other soil-forming factors such as parent material, climate, topography and time are similar.Total carbonate carbon did not differ at any depth or position along the ecotone, and the only variation in soil pH across the ecotone occurred in the 5-10cm depth increment.Bulk density varied along the ecotone in all depth increments except the 10-20cm.All other soil properties varied significantly along the ecotone, but only in the organic horizon, if analyzed, and 0-5cm depth.Therefore, the only significant difference in soil properties occurred in surface horizons, which can be used to hypothesize that the forest-grassland mosaic in the southwest Yukon is not driven by differences in soil.However, because this study examined the relationship between soil and vegetation by assuming that the southwest Yukon was a steadystate system, future research may wish to examine all state factors to confirm this assumption before further analysis is completed.Additionally, since the patterns of ecotonal and vertical distribution of soil properties appear to be linked to the patterns of organic matter, future research may consider quantifying controls on organic matter such as above and belowground plant allocation in order to gain a better appreciation of total ecosystem carbon dynamics and potential effects of climate change on soil.I would first like to thank my supervisor, Dr. Ryan Danby, for all his support, encouragement and knowledge in helping me complete all aspects of this thesis.His assistance in creating the sampling design, interpreting results within SPSS and editing of this manuscript was extremely helpful and ensured that proper data and results were obtained for this thesis.Additionally, I would like to thank my co-supervisor, Dr. Neal Scott, for his extensive knowledge in soil science and carbon and nitrogen cycling.This thesis would not have been possible without his help in determining laboratory methods and procedures, analyzing results and editing.Furthermore, I would like to thank Alix Conway, Ashley Lowcock and Lucas Brehaut for their advice and help with both field and the laboratory procedures.Additionally, thanks to Anthony Bassutti for patiently teaching me all the detailed methods and procedures necessary to run samples on the LECO elemental analyzer and Lyn Garrah for emotional support while analyzing ANOVA results and editing my poster presentation.Thanks to Karen Depew and Dr. Brian Cumming for coordinating meetings and providing continual support throughout the year.Thank-you to the Kluane Lake Research Station (KLRS) for an incredible fieldwork season this past summer and to the Queen's Summer Work Experience Program (SWEP) and NSERC grant to Dr. Danby for funding my stay
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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 | 0.001 |
| 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 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".