A comparison of alternative data sources for modeling site index in loblolly pine plantations
Bibliographic record
Abstract
Site index curves are generally developed from one of three data sources: (i) permanent sample plots, (ii) temporary sample plots, or (iii) stem analysis data. Permanent sample plots are considered to be the best data source for modeling height–age relationships; however, due to time and cost constraints, analysts may consider using temporary sample plots or stem analysis data for equation fitting. Temporary sample plot and stem analysis data, although more quickly obtained, require assumptions that are often not met when modeling site index. The question becomes how models developed from temporary sample plot or stem analysis data compare with models developed from permanent sample plot data. Data from a region-wide study in loblolly pine (Pinus taeda L.) plantations were used to develop site index curves from each of the three data sources. A form of the Chapman–Richards model was used for all three data sources to guard against confounding and to discern differences among the data sources when modeling height–age relationships. For the comparison and evaluation of behavior of different functions, the Schumacher model was also fitted to the three data sources. Curves developed from temporary sample plot and stem analysis data did not reproduce the height growth pattern exhibited in permanent sample plots, although curves derived from temporary plots were closer to the height growth pattern.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".