Contrasting current and potential productivity and the influence of fire and species composition in the boreal forest: a case study in eastern Canada
Bibliographic record
Abstract
The capacity of a forest stand to produce timber is related to the interactions that exist between its regeneration capacity, physical site characteristics (climate, surficial deposit, drainage), and disturbances. Minimally, to be sustainably managed, a forest needs to be sufficiently productive and able to regenerate after a disturbance so that its productive capacity is maintained or enhanced. To this effect, we evaluated timber productivity over a large area (175 000 km2) covering the latitudinal extent of closed-canopy black spruce (Picea mariana (Mill.) B.S.P) forest. Site index and relative density index were used to identify stands that cannot reach a minimum volume of trees of minimum size over one rotation. A nonparametric method was used to estimate their values for all stands within the study area. This imputation used either physical site attributes alone to assess potential productivity independent of stand history or physical and vegetation site attributes to assess current productivity. The proportion of productive stands was then estimated at the scale of landscapes ranging from 39 to 2491 km2. Physical site factors alone explain 84% of the variability in the percentage of potentially productive stands (78% for currently productive stands); their combination resulted in an abrupt transition in productivity over the study area. However, burn rate alone also explains 63% of variation in the proportion of currently productive stands and 41% of the relative difference between percentages of potentially or currently productive stands. These results have implications for strategic forest management planning at land classification stage, as timber production area is assumed to remain stable through time, whereas it is apparently related to the disturbance rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".