Improving precision of field inventory estimation of aboveground biomass through an alternative view on plot biomass
Bibliographic record
Abstract
Abstract We contrast a new continuous approach (CA) for estimating plot-level above-ground biomass (AGB) in forest inventories with the current approach of estimating AGB exclusively from the tree-level AGB predicted for each tree in a plot, henceforth called DA (discrete approach). With the CA, the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed-area plot is computed as the integral of the AGB surface taken over the plot area. Hence with the CA, the portion of the biomass of in-plot trees that extends across the plot perimeter is ignored while the biomass from trees outside of the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped two hectare area to illustrate that important differences in plot-level AGB estimates can emerge. Ideally CA-based estimates of mean AGB should be less variable than those derived from the DA. If realized, this difference translates to a higher precision from field sampling, or a lower required sample size. In our case study with a target precision of 5% (i.e. relative standard error of the estimated mean AGB), the CA required a 27.1% lower sample size for small plots of 100 m 2 and a 10.4% lower sample size for larger plots of 1700 m 2 . We examined sampling induced errors only and did not yet consider model errors. We discuss practical issues in implementing the CA in field inventories and the potential in applications that model biomass with remote sensing data. The CA is a variation on a plot design for above-ground forest biomass; as such it can be applied in combination with any forest inventory sampling design.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".