Spatial distribution of injuries to Norway spruce advance growth after selection harvesting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Injuries and mortality to advance growth (saplings) after selection harvesting was studied in 17 multistoried Norway spruce (Picea abies (L.) Karst.) stands. Harvest removals ranged from 33 to 67% of initial basal area. Four of the stands were harvested with a motor-manual method (chain saw and skidding with farm tractors; MFT). The remaining stands were harvested with single-grip harvesters and forwarders (HFW). In each stand, injury rates were evaluated on a 24 × 48 m plot, located between the centre lines of two parallel strip roads that were spaced 24 m apart. All logging teams had at least 5 years of experience in clear-cutting and thinning operations. The trees to be removed and the strip road centre lines were marked prior to harvest. Mortality varied from 5 to 51%, whereas total injury (injured + dead saplings) varied from 17 to 76%. Mortality and injury levels were generally highest on HFW plots. Crown reduction and leaning stems were the most frequent types of injury, regardless of operating method. Injury rates increased with sapling height with the HFW method, whereas the opposite was found on MFT plots. Saplings without preharvest damage in the form of top or leader defects had a higher probability of being injured than saplings with such damage in stands harvested with the MFT method. A similar difference was not found on HFW plots. A logistic regression model shows that the spatial risk of injury depends on the interaction between forest condition factors and operational characteristics. Forest condition factors influencing the risk of injury are sapling height and the location of saplings relative to larger residual trees and strip roads. Corresponding operational characteristics are operating method and harvest intensity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it