Estudio del efecto del sistema de cultivo en la salud del suelo en la Estación de Investigación Agrícola del Noroeste de Ontario (Canadá)
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
Anthropogenic activities impact soil in varying degrees, from preserving natural landscapes to intensive agriculture which among the farm practices that impact the soil are the cropping systems. Information on cropping systems and soil impacts in northern territories is still missing. This study assesses the effect of different cropping systems on soil health -physical, chemical and biological soil properties and indicators of soil health - at the Lakehead Agricultural Research Station [LUARS] in northern Ontario, Canada. The study compares three cropping systems (perennial crops-pasture, grass, and annual crops -wheat, barley, corn, soybeans) and two forest areas (conifer plantation and naturally regenerating mixed wood forest) at LUARS. Soil samples were collected at different depths and analyzed for various indicators using the Cornell Soil Health Assessment framework. The results showed the soil health scores varied among cropping systems, with natural forest and perennial crops-pasture having higher scores compared to annual crops -wheat, barley, corn, soybeans. Soil organic matter was found to be lowest in annual crops -wheat, barley, corn, soybeans, while aggregate stability was highest in natural forests. The study also identifies the soil health gap, which represents the difference between the health of a particular cropping system and a benchmark. The soil health gap analysis can help farmers implement practices to improve soil health and increase the resilience and sustainability of agroecosystems. Overall, this study emphasizes the importance of understanding the effect of cropping systems on soil health and provides insights into potential strategies for improving farm practices.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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