Boreal old-growth forest structural diversity challenges aerial photographic survey accuracy
Bibliographic record
Abstract
The erosion of old-growth forests in boreal managed landscapes is a major issue currently faced by forest managers; however, resolving this problem requires accurate surveys. The intention of our study was to determine if historic operational aerial forest surveys accurately identified boreal old-growth forests in Quebec, Canada. We first compared stand successional stages (even-aged vs. old-growth) in two aerial surveys performed in 1968 (preindustrial aerial survey) and 2007 (modern aerial survey) on the same 2200 km2 territory. Second, we evaluated the accuracy of the modern aerial survey by comparing its results with those of 74 field plots sampled in the study territory between 2014 and 2016. The two aerial surveys differed significantly; 80.8% of the undisturbed stands that were identified as “old-growth” in the preindustrial survey were classified as “even-aged” in the modern survey, and 60% of the stands identified as “old-growth” by field sampling were also erroneously identified as “even-aged” by the modern aerial survey. The scarcity of obvious old-growth attributes in boreal old-growth forests, as well as poorly adapted modern aerial survey criteria (i.e., criteria requiring high vertical stratification and significant changes in tree species composition along forest succession), were the main factors explaining these errors. It is therefore likely that most of Quebec’s boreal old-growth forests are currently not recognized as such in forest inventories, challenging the efficacy of sustainable forest management policies.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".