Effects of low and high nutrients on the competitive hierarchy of 26 shoreline plants
Bibliographic record
Abstract
Summary 1 We tested the hypothesis that competitive hierarchies are invariant with respect to changing nutrient supply. 2 The competitive performance of 26 shoreline plant species was determined experimentally as the relative ability to suppress the growth of a common indicator (phytometer) species, Penthorum sedoides. Each species was grown with the phytometer under each of two nutrient treatments created with different concentrations of a modified Hoagland’s solution (n = 5 replicates per species/treatment), for two growing seasons. 3 Although shifts in ranking of relative competitive performance were apparent between nutrient levels, competitive performance under high and low nutrient conditions was significantly correlated in both year 1 (r = 0.65) and year 2 (r = 0.76), when all species were considered. 4 At the broad community scale, the outcome of competitive interactions thus appears to be relatively predictable and independent of the environment, and therefore provides a useful tool for exploring and understanding community pattern. These results cannot address questions related to the outcome of competitive interactions between similar species or the effects of fine‐scale pattern.
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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 | 0.000 |
| 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) | 0.001 | 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".