Conflicting portrayals of remaining old growth: the British Columbia case
Bibliographic record
Abstract
Old growth is disappearing globally, with implications for biodiversity, forest resilience, and carbon storage; yet uncertainty remains about how much exists, partly because assessments stratify ecosystems differently, sometimes obscuring relevant patterns. This paper compares portrayals of British Columbia’s (BC) old-growth forest stratified in two ways: by biogeoclimatic variant, as per policy, and by relative site productivity. Our analyses confirm provincial government claims that about a quarter of BC’s forests are old growth but find that most of this area has low realized productivity, including subalpine and bog forests, and that less than 1% is highly productive old growth, growing large trees. Within biogeoclimatic variant, nearly half of high-productivity forest landscapes have less than 1% of the expected area of old forest. Low-productivity ecosystems are over-represented in protected forest. We suggest that the experiment of managing old growth solely by biogeoclimatic variant has failed and that current forest policy, in combination with timber harvesting priorities, does not maintain representative ecosystems, counter to the intent of both policy and international conventions. Stratifying old growth by relative productivity within biogeoclimatic variant seems an appropriate method to portray ecosystem representation, potentially increasing the probability of maintaining ecosystem resilience.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".