Comparison of geospatial and ground-based methods for determining postharvest dispersed woody residues
Bibliographic record
Abstract
Postharvest woody residues are measured to estimate billable waste, bioenergy potential, fuel loadings, and carbon budgets. In fall 2014, a waste and residue survey (WRS) established twenty-nine 0.4 ha plots in the dispersed residue stratum on two cutblocks on Vancouver Island, British Columbia, and measured woody residue “logs” ≥ 10 cm inside-bark diameter and ≥ 20 cm in length. A line-intersect sampling (LIS), in spring 2015, measured all woody debris ≥ 10 cm diameter outside bark (DOB) on 18 plots. High-resolution (2 cm) photography was acquired in summer 2015, orthophotomosaics were prepared and analyzed for residue “logs” ≥ 10 cm DOB in 29 plots using semi-automated “log” delineation (SLD) and manual heads-up “log” digitization (MLD). After adjustment for bark thickness, SLD values were still higher than WRS values, due to inclusion of non-log pieces, though MLD values were not. LIS values were not different from WRS values once adjusted for bark thickness, transect overlaps, and decayed or non-log pieces excluded. The LIS and preharvest forest cover species composition differed from the WRS. While the SLD geospatial method can census ≥ 10 cm diameter residues in entire cutblocks, it was biased. Field-based methods may be required to correct SLD bias and measure species composition to determine bark thickness and wood densities to calculate biomass from residue volumes.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".