Carbon Characterization as an Ecological Monitoring Tool in Essex, Ontario
Bibliographic record
Abstract
Carbon Characterization as an Ecological Monitoring Tool in Essex County, Ontario.\nEmily Browne University of Windsor\nCatherine Febria University of Windsor\nLauren Weller University of Windsor\n(Authorship order: Browne, E., Weller, L. and Febria, C.M.)\nWetlands were once a common feature in the Laurentian Great Lakes basin prior to agriculture intensification and may be an important restoration option to address water quality concerns. Wetlands provide several ecological services, such as flood mitigation, water purification, habitat provisioning for birds and aquatic species, as well as the ability to sequester from atmospheric CO2 in its’ sediments. Dissolved organic matter (DOM) is a component of both soil and water and reflects the origin and complexity of carbon and the roles it has in ecosystem functions. Florescence dissolved organic matter (FDOM) can reveal unique carbon signatures that indicate the complexity and likely source materials of carbon (e.g., terrestrial leaves, algae) and likely human activities (e.g., urbanization, agriculture). The objective of this study is to characterize carbon using FDOM properties in both agricultural and wetland soils in Essex, Ontario. Three agricultural fields were selected to represent a gradient of wetland and conservation agricultural approaches: conventional tillage, cover crops, and no tillage, and retired agricultural lands. All sites were otherwise located on Brookston clay soil. This study predicts: (1) the wetlands will have more complex FDOM signatures due to the older humic standing stocks of carbon in the soils, (2) agricultural fields will have simpler FDOM signatures due to the management practices and extensive use of the soil, and (3) the complexity of the FDOM signatures will vary with depth in the soil. FDOM was characterized using fluorescence spectroscopy to produce excitation-emission matrices for each sample (EEMs; n=111 samples). A multivariate model (parallel factor analysis) was employed to test my study predictions and compare FDOM signatures across varying spatial gradients at the site level and across sites. Understanding the properties of restored wetlands could help inform the importance of soil quality and health in restoration projects in Essex County, Ontario and more broadly in restoration efforts in agricultural landscapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".