Fertilizing bush beans with locally made compost in a remote subarctic community
Bibliographic record
Abstract
Abstract High latitude communities are cultivating crops to adapt to global warming, and thereby reduce dependency on food importation. To minimize the dependency of imported soil nutrient amendments for crop production, the Indigenous subarctic community of Fort Albany First Nation generated compost using by‐products from the traditional activities of goose harvesting along with other organic waste within the community. The compost was evaluated in a single growing season pot experiment as an amendment for bush beans (Phaseolus vulgaris L.) by being mixed with the local Terric Haplosaprist edaphic soil that was P and K deficient. Twelve pots growing bush beans were amended with compost at rates ranging from 3 to 30% and with unamended controls. All eight plant metrics (height, aboveground, leaf and bean biomass, quantity of leaves and pods, and individual and summed leaf surface area) showed a significant positive relationship with increasing compost amendments (p ≤ .0025, r2 = .66–.86), suggesting soils with compost attain greater bean yields than unamended soil. A threshold of bean growth was not reached, implying that compost amendments >30% may provide even greater bean yield. However, the application of P and K with the 30% compost addition exceeds recommended rates, suggesting that nutrient availability was hindered. Notwithstanding logistical issues in scaling‐up to amend all gardens in the community, such as improving the compost quality and quantity, composting using Indigenous harvest by‐products and local organic wastes is a promising adaptive food security strategy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".