Prediction of Douglas-fir fertilizer response using biogeoclimatic properties in the coastal Pacific Northwest
Bibliographic record
Abstract
Fertilizer response of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) has been related to site and soil properties in the past, but the models have explained only about half of the variation in these investigations. Fertilizer response and percent response after two years were examined, according to mapped and measured biogeoclimatic variables, for 71 Douglas-fir installations in the coastal Pacific Northwest. Paired-tree installations consisted of 12–20 pairs of Douglas-fir trees with one tree fertilized with urea at 224 kg N·ha−1. Pearson correlation coefficients and boosted regression tree (BRT) models were used to determine the best predictor variables and models of Douglas-fir fertilizer response. The BRT models, using the combination of mapped and measured variables, performed the best for predicting fertilizer response. Basal area and volume responses were most related to high forest floor and surface soil carbon to nitrogen ratios. Basal area mean annual increment (MAI) and site index were both negatively correlated with fertilizer response. Also, low basal area MAI was the most important tree measurement for predicting fertilizer response in the BRT models. Installations with many of the predictors from the BRT models (>66% of the model criteria) were found to have a significantly greater fertilizer response than installations with only a few predictors (<33% of the model criteria). These findings support the use of these models for predicting fertilizer response of similar Douglas-fir stands in the coastal Pacific Northwest.
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.
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 | 0.001 |
| 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) | 0.000 | 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 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".