Soil substrate selection for urban trees under deicing salt and compaction conditions
Bibliographic record
Abstract
The effects of high sodium chloride (NaCl) levels on Na and nutrient retention of three soil substrates and on littleleaf linden growth in different soil substrates were studied in the laboratory and the greenhouse, within the framework of a substrate selection program for trees planted in downtown Montreal. In addition, the effects of the structural soil (SS) compaction were studied in the first greenhouse experiment. The first greenhouse experiment was established in a factorial arrangement of four soils x four salt levels in a completely randomized design with five replicates. The four soil types were (1) L with a bulk density (BD) of 1.53 g/cm3, (2) LP with a BD of 1.26 g/cm3, (3) SS with a BD of 1.8 g/cm 3 (SS1.8), and (4) SS with a BD of 2.0 g/cm3 (SS2.0). The second greenhouse experiment had a split plot design. Two fertilization levels (with fertilizer and without fertilizer) were randomly arranged as the main plots on ten benches. Within each main plot, the combinations of three NaCl levels (0, 0.5 and 1.0 g NaCl/kg soil) and three soil substrates (L with a BD of 1.40 g/cm3, LP with a BD of 1.13 g/cm 3 and SS2.0) were randomized in the sub-plots. Results from the laboratory leaching experiment indicated that SS had the fastest Na leaching rate and highest Na loss, while LP retained more Na than SS or L. (Abstract shortened by UMI.)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".