Sugar maple seedling regeneration of a natural cohort across a latitudinal gradient in New Hampshire
Bibliographic record
Abstract
Sugar maple, an economically and ecologically important tree in the northern hardwood forest, has experienced regeneration failure that in the Northeast portion of the range has been variously attributed to soil acidification and resultant changes in soil chemistry, impacts of climate change, and effects of species composition. In a 5-year study spanning a latitudinal gradient in the state of New Hampshire, we examined evidence for these three hypotheses to explain sugar maple regeneration patterns. Overall, sugar maple seedling survival was highest in the two sites with lower sugar maple abundance. Alternatively, the two other sites with greater than 50% sugar maple relative dominance shared the following outcomes: higher seed production per area, greater foliar pest damage, lower seedling survival, lower sapling density, and higher canopy maple mortality, while the sites with lower dominance of maple had opposite outcomes. Based on field data and a common garden experiment, conspecific impacts on seedling survival were related to foliar pests and fungal pathogens rather than through soil feedbacks. These results lend support to other studies encouraging promotion of stand tree diversity and avoidance of monocultures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".