Detecting Change in Forest Floor Carbon
Bibliographic record
Abstract
Changes over time in forest soils are important to global C balance and to local ecosystem function. Detecting change in C storage in the forest floor is hampered by high variability and the use of study designs that are not adequate to statistically detect change. Using estimates of variability from previous forest floor studies, mostly conducted in the northern USA and Canada, we conducted statistical power analyses to assess the ability of such studies to detect various magnitudes of change in forest floor C. The studies we surveyed were unable to detect statistically significant changes in forest floor C or mass smaller than 15 to 20%. Studies that remeasure plots or sites (i.e., paired designs) have greater statistical power to detect changes than those in which experimental units are independently located for the two sampling dates. The causal mechanisms of forest floor change influence the magnitude of the change, and accordingly our ability to detect such changes. The direct effects of climate change may be too small to be detectable by current designs, but larger changes in forest floor mass resulting from forest management, changes in tree species, changes in fire regime, or the introduction of earthworms are more likely to be detectable. With paired resampling and more efficient allocation of sampling effort, it should be possible for future studies to detect smaller changes.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".