Factors controlling litter production in a large Mediterranean river floodplain forest
Bibliographic record
Abstract
Although litter production is an essential floodplain forest function, the drivers and their relative importance remain largely unknown, especially in semi-arid rivers. The influence of forest structure, flooding regime, soil conditions, and litterfall chemistry (a total of 17 variables) on spatial variability of litter production within the Middle Ebro River floodplain forests (northeastern Spain) was examined by monitoring litterfall in 12 forest plots in 2007. Linear mixed effects (LME) models, using stem density (SD), river distance (RD) (or soil total organic C (TOC)), and soil total P (TP) as independent predictors, explained 51% of the variance in litter production, while an alternative LME model using SD and P use efficiency (P-NUE) explained 40%. In particular, litter increased with SD and TP and decreased with RD, P-NUE, and TOC. Based on these results, P limitation appears to be controlling litter production in a rather dry hydrological regime. We hypothesized that a deficient sediment input at the plot scale (especially in the outer floodplain), with flood quality (overbank flooding, ground-water seepage, ponding) being a greater determinant than quantity (flood duration, water table levels), might ultimately be responsible for the spatial variability observed in litter production.
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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 | 0.001 | 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".