Bibliographic record
Abstract
ii This study examined the yields of two biofuel crops, canola and sunflower, as a function of the thickness of covers of municipal compost over nickel and copper mine tailings at the Xstrata Onaping Tailings Facility, northwest of Sudbury. I hypothesized that yield and health of each crop would decrease over thinner compost covers and would likely show a threshold. In the spring of 2012, a cover of municipal compost was applied over 2.8 ha of tailings to create a compost thickness gradient, running from ~65cm to 0 cm. Canola and sunflowers were seeded in strips along this compost thickness gradient. Around 35 plots were established for each crop, with ~7plots per 15 cm compost thickness interval. In each plot, I measured soil conductivity, pH, water content and bulk density as independent variables and also measured germination, chlorosis, presence of insects, crop height and the aboveground dry biomass of each crop as dependent variables. pH and conductivity did not vary much across the compost cover. Using simple and multiple regression analyses, germination and height was not a function of any of the independent variables, including compost thickness. For dry aboveground biomass, canola was also not significantly affected by any independent variables, but for sunflower, aboveground biomass was significantly higher on thinner biosolids covers. Both of these results are contrary to our original hypotheses. If correct, it may be possible to apply compost covers more thinly, without an impact on the yield of annual biofuel crops. This experiment should be repeated to verify this result across another growing season with different weather conditions. iii
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.191 | 0.110 |
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".