Accounting for surveyor inconsistency and bias in estimation of tree density from presettlement land survey records
Bibliographic record
Abstract
Presettlement land survey records provide baseline data on forest characteristics prior to major European settlement, but questions regarding surveyor bias and methodological consistency have limited confidence in quantitative analyses of this important data source. We propose new correction factors, calculated from bearings, distances, and species of bearing trees, to account for the effects of (i) inconsistency in quadrant configuration, (ii) bearing angle bias, and (iii) species bias on forest density and species composition estimated from presettlement land survey records. Computer simulations confirmed accuracy in random and nonuniform density forests, with moderate bias in very clustered and dispersed forests. A case study of township and quarter-section corners surveyed by the Holland Land Company in western New York demonstrates the potential magnitude of errors caused by surveyor inconsistency/bias in estimation of density and relative species frequency. The influence of nonuniform density, clustering, and dispersal on plotless density estimators remains an important obstacle to quantitative analysis of Presettlement land survey records. However, by accounting for uncertainties regarding surveyor methodology, the proposed correction factors add confidence to conclusions made regarding presettlement forest structure and composition.
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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 | 0.048 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".