Real-time monitoring of deadwood moisture in forests: lessons learned from an intensive case study
Bibliographic record
Abstract
Attributes of deadwood in forests, including quantity, landscape position, and state of decay, influence numerous ecosystem processes such as wildfire behavior, tree regeneration, and nutrient cycling. Attributes of deadwood that vary over subdiurnal time steps, including moisture, have not been routinely measured despite the profound effects they have on ecosystem processes. To improve our understanding of forest deadwood subdiurnal moisture dynamics, we installed an intensive time-domain reflectometry (TDR) sensor network in a log and surrounding soil within a northern hardwood forest in New England, United States. Intensive monitoring during a partial growing season indicated that deadwood moisture was dynamic but similar to that of surrounding soils at 15-min intervals, especially during wetting and drying events. Field results and bench analysis of the sample log revealed numerous challenges when attempting to monitor deadwood moisture with TDR such as heterogeneous and (or) advanced decay confounding TDR moisture measurements in logs. An efficient, high-frequency TDR sensor network was demonstrated to record deadwood and soil moisture fluctuations, which provides an opportunity to refine our understanding of deadwood dynamics in the context of global change such as changing precipitation regimes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".