Temporal stability of stratifications using different dendrometric variables and geostatistical interpolation
Bibliographic record
Abstract
Stratifying a forest results in more precise and cheaper inventories. This study aimed to select the stratifying variable that estimates more precise and stable inventory over the years for a eucalyptus plantation in Minas Gerais state, Brazil. The continuous forest inventory was performed annually from 2.7 to 6.8 years, and based on the field measurements, arithmetic mean diameter (d), height (h), dominant height (Hdom), basal area (G), volume (V), and mean annual increment in volume (MAI) were calculated. Semivariograms were generated and the exponential, spherical and Gaussian models were fit for each stratifying variable for each measurement date. The models were assessed by the reduced mean error and its deviation, being the exponential model selected. Maps showing the spatial distribution of all variables were generated for each measurement age, using ordinary kriging. Next, the study area was divided in four strata based on each stratifying variable for each measurement age. The stability of each stratifying variables for each measurement age were assessed by: 1) coincident strata area; 2) stability of total strata area; 3) plot permanency on each stratum; and 4) inventory error using stratified random sampling procedures. All variables in all ages presented spatial dependence structure. G and Hdom were the stratifying variables that generated the most and the least coincident strata area over the years, respectively. G and height (h and Hdom) were the stratifying variables yielding the least and most plot stratum changes, respectively. The same trend was observed for the total strata area stability. Stratifying based on MAI and V yielded the smaller inventory error, and h and Hdom yielded the largest. G was selected as the best stratifying variable because it yielded small inventory errors and was the most stable variable in terms of coincident strata area, total strata area and plot stratum changes over the years.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".