Agriculture and the Life Aquatic: Effects of Agricultural Landscape Structure on Farmland Aquatic Biodiversity and Water Quality
Bibliographic record
Abstract
This thesis is formatted using the integrated thesis format, and therefore each data chapter was written as an independent manuscript.Chapters 2 and 3 have been published in, and Chapter 4 has been submitted to, a peer-reviewed journal when this thesis was completed.The text of each published chapter has been reproduced in the following thesis in whole, but with minor changes to formatting.There is some repetition in the introductions, methods and discussions; however, I have cross-referenced between chapters to reduce repetition as much as possible.Each data chapter is a co-authored work; however, I performed the majority of the work presented in this thesis.I proposed and developed all research questions and hypotheses, in cooperation with my supervisor, Dr. Lenore Fahrig.I was primarily responsible for the design of projects to address these research questions.I carried out all field work and collected the majority of the data for Chapters 2, 3 and 4. I analysed all of the data and wrote all first drafts of the data chapters.The contributions of my co-authors are as follows:(1) Dr. Lenore Fahrig (Carleton University) contributed to all data chapters, helping to develop research questions and hypotheses.She also provided guidance during project design, and contributed significantly to the writing of each chapter.(2) Dr. Gregory Mitchell (National Wildlife Research Center) contributed to Chapter 3.He provided significant guidance during the statistical analysis of the data and contributed to the writing of this chapter.v (3) Lindsay Bellingham contributed to Chapter 3.She contributed to the design of the Ceriodaphnia dubia laboratory bioassays and was the primary researcher who conducted those experiments and collected the C. dubia population data.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".