Seasonal nutrient transfers by foliar resorption, leaching, and litter fall in a northern hardwood
Bibliographic record
Abstract
Abstract: A descriptive temporal model was used to evaluate the flow of macronutrients (N, P, K, Ca, and Mg) be-tween the forest canopy and incident precipitation for the Lake Clair Watershed (LCW) located in the northern hard-wood forest region of Quebec, Canada. The model also quantified the resorption mechanism. Wet precipitation, throughfall, foliage, and litter fall data for 1997 were used to quantify the following: (1) dry deposition intercepted by forest cover (0.38, 0.07, 0.07, and 0.03 kg·ha–1 for Ca, K, Mg, and P, respectively); (2) leaching from foliage (1.81, 6.46, 0.48, and 0.13 kg·ha–1 for Ca, K, Mg, and P, respectively); and (3) foliar resorption (N = 65%, P = 65%, K = 42%, Mg = 30%, and Ca = 10%). Foliar N, P, and K pools increased after bud break and remained constant until mid-September when they decreased rapidly. The foliar Ca pool increased until leaf fall, while the foliar Mg pool reached a maximum in early July and decreased slowly until leaf senescence. Phosphorus, K, Ca, and Mg were leached from the canopy whereas N from wet precipitation was retained by the canopy. The relatively high Mg and Ca resorption rates are consistent with the low soil Ca and Mg availability reported at the LCW. Consideration of leaching and dry deposi-tion, as well as the temporal dimension, demonstrated the importance of each of these parameters for increasing the ac-curacy of the foliar nutrient resorption estimates. Résumé: Un modèle temporel descriptif a été construit afin d’évaluer les principaux flux des macro-nutriments (N, P, K, Ca et Mg) entre le couvert forestier et la précipitation incidente pour le bassin versant du lac Clair, situé dans la
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".