From streams to citizens : a multi-lens investigation of water quality through carbon cycles and participation within water science and policy
Bibliographic record
Abstract
Water management approaches that are scientifically sound and societally relevant are critical, given the myriad demands and threats posed to this resource and its necessity for environmental and human life. This thesis reflects the inherently complex and multifaceted nature of water management by investigating issues of water quality, as well as societal participation within science outlining the health of water resources, and policy that determines admissible human impacts. First, the technical details of deploying in situ water quality monitoring networks are examined, given challenges of installing sensitive and costly sensors within remote and physically dynamic stream environments. A year of high frequency measurements explicates the necessity of measuring concentrations at adequate time intervals to accurately calculate fluxes of dissolved organic carbon (DOC). Secondly, these spectrophotometric approaches were used to investigate how forest harvest affects in-stream DOC biogeochemistry within a small headwater stream on Vancouver Island, British Columbia. Forest harvest has a large impact on catchment biogeochemistry and hydrology. Harvest increases DOC concentration and flux within the stream. It also alters the chemical composition of DOC, signifying impacts on the catchment scale mechanisms by which DOC is created and transported. Thirdly, the impacts of citizen participation on scientific data outcomes within the burgeoning field of citizen science are detailed. Six critical lessons learned were distilled from a citizen science water quality monitoring program (concerning DOC concentration and characteristics). Scientific data was used alongside qualitative vignettes to explicate the importance of citizen perspectives, contextual knowledge, and motivations for involvement on critical data outcomes. Lastly, the process and outcomes of public participation through consultation in the creation of provincial-level water policy are discussed. The extensive public consultation process undertaken during the modernization of BC’s Water Sustainability Act was used to examine what was expressed during consultation, and how this compared to the contents of the Act. Differences between consultation outcomes and Act contents (especially related to water allocation) bring to bear questions regarding the function of consultation within participatory policy processes, including how consultation was (and should be) used, and the possible influence of elite groups.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".