Assessment of weather-associated causes of red spruce winter injury and consequences to aboveground carbon sequestration
Bibliographic record
Abstract
Despite considerable study, it remains uncertain what environmental factors contribute to red spruce ( Picea rubens Sarg.) foliar winter injury and how much this injury influences tree C stores. We used a long-term record of winter injury in a plantation in New Hampshire and conducted stepwise linear regression analyses with local weather and regional pollution data to determine which parameters helped account for observed injury. Two types of weather phenomena were consistently associated with elevated injury: (i) measures of low-temperature stress that incite injury and (ii) factors that reduced the length of the growing season and predisposed trees to injury. At this plantation, there was a significant linear relationship between winter injury and growth reductions for 2 years after a severe winter injury event. Analysis using data from three New England states indicated that plantation data reflected a regional response. Using regional data, we estimated a reduction of 394 000 metric tons of C sequestered in living red spruce stems ≥20 cm in diameter growing in New York and northern New England during the 2 years following a severe winter injury event. This is a conservative estimate of reduced C sequestration because injury-induced mortality and other factors were not evaluated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".