Uncertainty in photo-interpreted forest inventory variables and effects on estimates of error in Canada’s National Forest Inventory
Bibliographic record
Abstract
Canada’s National Forest Inventory (NFI) relies on photo-interpreted forest resource data provided by provincial and territorial agencies. NFI data are collected at regular intervals in time from a nominal 20 × 20 km network of 2 × 2 km photoplots. Attribute-specific NFI estimates of precision include contributions from sampling errors and uncertainty in the source data. We assessed this uncertainty in NFI photo-interpreted forest attribute data from New Brunswick and Nova Scotia. Attributes examined were: cover type, age, maturity (class), crown closure, height, volume, and area associated with an attribute. Monte-Carlo simulations, with measurement errors superimposed on NFI data assumed to be error-free, showed that estimates of precision were inflated by an average of 7% (range 0%–36%) due to the uncertainty in the source data. Species misclassification and age determination were the largest sources of uncertainty.
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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 | 0.090 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".