Using Aquatic Mesocosms to Assess the Effects of Soil and Vegetation for Informing Environmental Research
Bibliographic record
Abstract
An aquatic mesocosm facility consisting of thirty 15,000 L tanks was constructed in Vegreville, Alberta to support environmental research. In 2017, an experiment was conducted as an inaugural run for the facility; this study continued through the winter of 2017/18 (over-wintering is a unique capability of the facility) and concluded in the fall of 2018. Here, we report key methods used to evaluate the effects of two independent variables: (1) a soil layer covering the floor of the mesocosms, and (2) vegetation installed in the mesocosms. Although a range of response variables were measured during this study, we limit our analysis here to the physicochemical (e.g., pH, turbidity, conductivity, and dissolved oxygen) and biological/ecological response variables (e.g., macrophytes, phytoplankton/metaphyton, and macroinvertebrates) that differed due to these two variables. The presence of a soil layer covering the floor of the mesocosm was associated with increased turbidity on some days and depths in 2017. Specific conductivity was higher in the presence of soil and its associated adventitious vegetation. During this initial study, we gained a better understanding of the characteristics and mechanics of the mesocosms, which informs design and implementation of future experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".