Bibliographic record
Abstract
Soil organic matter (SOM) can be a great indicator of soil quality. However, land cultivation canresult in a decrease in SOM, thus decreasing farmers’ ability to grow crops. To combat this,farmers employ techniques to increase the SOM content of their soil. To understand theeffectiveness of these techniques and the effect of cultivation on SOM content, we tested theSOM content of farm soil and uncultivated soil from backyards under the hypothesis that farmsoil would have higher SOM content. The soil was sampled from two different general locations:Squamish and Vancouver. In each location, three soil samples were taken from two differentfarms and a backyard. In Squamish, the farms were Lavendel and Nutridense. The farms inVancouver were Snow and UBC. Samples were dried and then reacted with hydrogen peroxideto remove SOM through oxidation. The mass of the soils pre and post-treatment with hydrogenperoxide was compared to determine the SOM percentage in each sample. All of the data fromboth general locations were grouped into either farm data and backyard data. The mean SOMpercentage for the farm group and backyard group was calculated as 1.825 and 1.098respectively. An unpaired t-test was then run on the data to determine whether the difference inmeans between the two groups was significant. After calculating a p-value of 0.1806, wedetermined the difference in means was not significantly significant. Therefore, we failed tosupport our hypothesis that farming techniques increase SOM content and concluded thattechniques employed by farmers are not effective in raising SOM past pre-cultivation levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".