Increasing amounts of coir dust in substrates do not improve physical properties or growth of tree seedlings in a novel air-pruning propagation tray1
Bibliographic record
Abstract
Abstract Air-pruning can improve tree seedling root quality in propagation by subjecting root tips to desiccation, thereby avoiding deflections, but also increases substrate dry-out rates. Several studies have indicated that coconut ( Cocos nucifera L.) coir dust can enhance water holding properties, possibly benefiting trees grown in air-pruning trays. However, water availability characteristics are influenced by particle size. In this experiment, coir dust was added into a sphagnum peat-perlite substrate mix at rates of 10, 15 and 20%. An industry standard peat-perlite mix was tested as a fourth substrate type. Red oak ( Quercus rubra L.), red maple ( Acer rubrum L.), quaking aspen ( Populus tremuloides Michx.) and eastern white cedar ( Thuja occidentalis L.) were grown from seed in these four substrate types. Physical and chemical properties of all substrate types were analyzed pre-experiment. The particle size distribution was finer and more even in the peat-perlite mix compared to the three coir mixes. The higher proportion of coarse particles in the 20% coir mix may have reduced water availability. Seedlings grown in the 15 and 20% coir mixes had lower above and below-ground growth compared to the 10% coir and peat-perlite mixes in all species except red oak. Index words: soilless media, water holding capacity, air space, particle size distribution, chlorophyll content, tree growth. Species used in the study: red oak ( Quercus rubra L.); red maple ( Acer rubrum L.); quaking aspen ( Populus tremuloides Michx.); eastern white cedar ( Thuja occidentalis L.).
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".