Using Aquatic Mesocosms to Assess the Effects of Soil and Vegetation for Informing Environmental Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it