Assessing legacy effects of a 12‐year irrigated cropping systems study with a post‐hoc bioassay
Bibliographic record
Abstract
Abstract A healthy soil resource is vital to the continued success of irrigated agriculture in southern Alberta. A 12‐yr (2000–2011) irrigated cropping systems study was followed with a dry bean (Phaseolus vulgaris L.) field bioassay in 2012, to assess legacy effects of preceding management. Specifically, a comparison of conventional (CONV) and conservation (CONS) management (reduced tillage, cover crops, compost addition, narrow‐row dry bean) legacies was sought. However, rotational legacies such as preceding phase, length, preceding crop, and interval since previous dry bean were also assessed by the fully phased experimental setup. Only 1 of 18 possible soil management contrasts (CONV vs. CONS, 2000–2011) was significant for the dry bean bioassay in 2012, despite overwhelming evidence of improved soil health (microbial biomass C and β‐glucosidase activity) under CONS management. Monoculture wheat (Triticum aestivum L.) from 2000 to 2011 led to 2 d earlier maturity and higher disease incidence in bioassay dry bean. With wheat as a preceding crop, bioassay dry bean was significantly shorter (4 cm), earlier maturing (2 d), and lower yielding (by 21–35%) than with dry bean, potato (Solanum tuberosum L.), or sugar beet (Beta vulgaris L.) as preceding crops, largely due to volunteer wheat competition. Significantly enhanced bioassay yields (9–13%) with shorter intervals since previous dry bean demonstrated a “legume effect.” Overall, the dry bean bioassay was less effective at assessing soil management legacies (CONV vs. CONS) than rotational legacies such as preceding crop or interval since previous dry bean.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".