Analysis of fine rooting below skid trails using linear and generalized additive models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soil compaction caused by forest machinery changes the basic conditions for root propagation below skid trails. In consequence, lower fine-root densities have to be expected under wheel tracks compared with other skid trail strata that experience no direct traffic. Explorative data analysis of fine-root densities below a skid trail revealed that the fundamental assumptions for linear modelling were violated. Using a generalized linear model following a Poisson distribution with a log link function for the predictor variables together with an exponential covariance function to cope with spatial autocorrelation, the formal model criteria were met. In contrast to the linear models, generalized additive models provide flexible surface estimators that enable us to model continuous response surfaces. In addition, generalized additive models allow for the calculation of confidence intervals for the estimated density surface and for the use of inferential statistics, such as comparisons between depth gradients of fine rooting at distinct transect locations or depth layers. These model characteristics improve the possibility to recognize differences and to evaluate fine-root disturbances below skid trails without integrating uncertain strata information. They also enhance the options for determining the duration of time that is necessary to restore the rooting capacity on formerly compacted soils.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 it