The Influence of Concrete Grinding Residue on Soil Physical Properties and Plant Growth
Bibliographic record
Abstract
Diamond grinding is a concrete pavement maintenance technique, and concrete grinding residue (CGR) is the byproduct. Concrete grinding residue deposited along roadsides affects soil chemical properties, but impacts of CGR on soil physical properties and plant growth are rarely studied. In this study, a controlled field experiment was performed to determine the influence of CGR on selected soil physical properties (i.e., bulk density [ρ b ], saturated hydraulic conductivity [ K s ], and water infiltrability [ I t ]) and on plant biomass and plant coverage under four application rates (0, 2.24, 4.48, and 8.96 kg m −2 ). Field measurements were performed before the CGR applications, and 1, 7, and 12 mo after the CGR applications. No significant CGR effects on soil physical properties were detected. The ρ b was relatively stable for all of the treatments, whereas some nonsignificant variations (e.g., 10–30% of mean K s values and mean I t values among four CGR rates) were found. Plant biomass with a CGR rate of 2.24 kg m −2 tended to be 10 to 40% larger than biomass in the control treatment, whereas plant biomass with a CGR rate of 8.96 kg m −2 tended to be ∼10% smaller than the control treatment. Concrete grinding residue had no significant effects on plant coverage, richness, Simpson's diversity, and evenness. Thus, CGR applications up to 8.96 kg m −2 did not significantly affect soil physical properties and plant growth in this controlled field study. This study can serve as a reference for results obtained from roadsides in Minnesota and Iowa that receive CGR applications. Core Ideas Concrete grinding residue (CGR) was evaluated in a controlled field study. CGR applications <8.96 kg m −2 did not significantly affect soil physical properties. CGR applications did not significantly affect plant biomass and community properties. The results provided a reference for diamond grinding in Minnesota and Iowa.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".