Analyzing the effects of stand thinning on microclimates with semiparametric smoothing splines
Bibliographic record
Abstract
Monitoring the effects of stand thinning on microclimates is an integral part of any thinning experiment. It is through its modifications of microclimates that thinning alters important ecological processes. An efficient analysis of microclimate-monitoring data should address both the effects of thinning regimes on, and the temporal response trends of, microclimates. Probably because of the difficulties in modeling temporal trends parametrically, an examination of the existing literature on thinning showed that only a few studies have attempted to address the second aspect. We propose the use of semiparametric smoothing splines to analyze monitoring data from thinning experiments. First, the concept of a smoothing spline is briefly described. We then provide an example in which semiparametric mixed-effects smoothing-spline models were used to analyze microclimate-monitoring data from a thinning experiment. The proposed approach not only successfully detected the effects of thinning, but also revealed interesting temporal trends. For each of the microclimatic variables, we also compared the performance of the fitted semiparametric model with that of a parametric model. In general, the semiparametric model performed better than its parametric counterpart. We also addresse some concerns in using the proposed approach.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".