Biochar as a growing media component for containerized production of Douglas-fir
Bibliographic record
Abstract
In the inland northwestern United States (US), Douglas-fir artificial regeneration commonly includes growing seedlings in media containing sphagnum peat. Concerns over the sustainability of peat and rising plant production costs are initiating investigation of growing media alternatives. Biochar is a potential media amendment that has positive physical and chemical properties for seedling production, including high water and nutrient retention due to large surface area, which may reduce leaching losses and improve fertilizer use efficiency. We used different amounts of biochar to amend peat-based growing media to determine if seedling growth response to various fertilizer rates differed with biochar amendments. Every 13 weeks for 39 weeks, replicate seedlings were measured for photosynthetic activity, destructively harvested, and analyzed for leaf nitrogen concentration. Biochar did not reduce fertilizer rates required to grow equal-sized seedlings or improve seedling growth. When mixed with peat at rates of 25% or 50% by volume, biochar progressively reduced height and diameter growth rates, seedling biomass, and photosynthetic rate. Biochar increased growing media pH to levels incompatible with conifer seedling requirements and decreased media extractable P concentration, which may have caused decreased photosynthesis. Adjusting pH of the biochar used would be necessary to grow Douglas-fir seedlings for forest regeneration.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".