Bibliographic record
Abstract
According to traditional theory, larger plants generaly have an advantage with respect to competition, especially for light. It seems a paradox then that most species that coexist within natural vegetation are relatively small; specis size distributions are right-skewed at virtually every scale. The critical question then becomes: if bigger is better in competition for resources, why then are there so many small plants? A potential explanation for this paradox is that smaller species may have greater reproductive economy-i.e. the ability to reproduce despite suppression from intense competition. Selection for greater reproductive economy may be associated with smaller seed sizes, increased rates of self-fertilization and/or clonality, and in the case of this study, smaller size at reproductive maturity. Random plots in an old field at Queen's University Biological Station were sampled and the largest and smallest reproductive individuals of each species were collected, dried and weighed - to test the hypothesis that smaller species can reproduce at a smaller proportion of their maximum potential plant sizes. The results did not support this, but the hypothesis that smaller plants have greater reproductive economy could not be rejected as it was not possible to record data for the largest possible plant size for each species (since even the largest plants were subjected to competition from neighbours). This provides a focus for future research. Understanding the role of plant size in affecting the process of species assembly has important implications for species coexistence and mechanisms of biodiversity preservation, and thus efforts involving conservation and ecosystem management.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".