Factors influencing black spruce reproductive potential in the northern boreal forest of Quebec
Bibliographic record
Abstract
The reproductive ecology of the semi-serotinous species black spruce ( Picea mariana (Mill.) BSP) in northern boreal forests remains poorly understood. There is a general lack of data on cone/seed production and viability as a function of biotic tree-level characteristics and abiotic variables. No studies currently exist to quantify these differences over a large gradient in temperature, elevation, and precipitation. Extensive physical, ecological, dendrometric, and reproductive data were collected from young to very old black spruce stands in northern Quebec. ANOVA and general linear mixed models were used to examine interannual cone production, and the relative importance of the biotic and abiotic explanatory factors in determining total cone production; length of the cone-bearing zone; filled seeds per cone; proportion of filled seeds; and seed viability. The results illustrate that the reproductive ecology of black spruce in northern cold forests is mainly explained by biotic variables such as age and diameter at breast height, and by abiotic variables related to temperature such as elevation, length of the growing season, and growing degree-days. Black spruce exhibits a lower reproductive potential in northern cold forests, making it possibly less resilient to increased fire frequency, particularly in unproductive and very young or very old stands.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".