Understanding Effects of Competition and Shade Tolerance on Carbon Allocation with a Carbon Balance Model
Bibliographic record
Abstract
A carbon-balance model based on mechanistic and allometric relationships (CroBas) was used to assess the effects of competition in C allocation in jack pine (Pinus banksiana Lamb.), a shade-intolerant species, and black spruce (Picea mariana (Mill.) B.S.P.), a moderately shade-tolerant species. For both species, model efficiencies ranged from 36 to 99%. The average model bias was lower than 11% and 18% for jack pine and black spruce, respectively. For both jack pine and black spruce, the total tree C increased over the years, with greater increases noted for decreasing competition. When considering a C compartment as a ratio of the total tree C, decreasing competition resulted for both species in decreasing stem C and increasing C in branches and foliage. When considering the amount of C in a given compartment, for jack pine, decreasing competition led to greater C stem, branches, foliage, and roots, whereas, for black spruce, it also increased its stem C but lately shifted at about 20 years, following thinning; thus, the changing C allocation over time results from both “passive plasticity”, reflecting environmentally induced variations in growth, and “ontogenetic plasticity”, referring to variations in the ontogenetic trajectory of a trait. Overall, the C allocation to stem and foliage relative to the total tree C generally decreased as competition decreased, supporting the optimal partitioning theory. These C-allocation patterns were related to the species’ shade tolerance and illustrated how jack pine and black spruce maximize their competitive fitness.
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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".