Using foliar variables to predict the response of lodgepole pine to nitrogen and sulphur fertilization
Bibliographic record
Abstract
Six-year basal area responses to nitrogen (N) fertilizer, alone and in combination with sulphur (S), in 31 lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) stands in the interior of British Columbia were compared with pre-treatment measures of foliar N and S and first-year increases in fascicle mass to determine the utility of these variables for predicting growth response and for determining appropriate fertilizer prescriptions. Results indicate that pre-fertilization levels of foliar N or inorganic sulphate-S (SO4) may be more reliable than the fascicle mass screening method for predicting whether or not a significant stemwood response will occur following N fertilization. When combined, N and SO4 levels in unfertilized foliage explained 68% of the variation in relative basal area response to fertilization with N alone. Foliar nutrients were also useful for determining whether or not lodgepole pine would respond incrementally to S when added in combination with N. Stands in which pre-fertilization foliar SO4 was [Formula: see text]60 mg/kg and N/S ratio was [Formula: see text]13 did not respond significantly to N alone but always responded significantly to N + S. Conversely, a foliar SO4 level >60 mg/kg combined with a N/S ratio of [Formula: see text]12 always resulted in a favourable response to N with no incremental benefit of added S.
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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 | 0.001 |
| 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 | 0.000 | 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 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".