A test of physical limitation to specific substrata during establishment for<i>Didymodon johansenii</i>, a rare moss
Bibliographic record
Abstract
The rare moss, Didymodon johansenii, occurs only on substrata with a fine layer of calcareous silt. In Alberta, these substrata are mainly logs and tree bases. Such substratum specificity patterns may relate to species interactions with the substratum itself, with other species, or a combination. Direct species–substratum interactions are most crucial during the establishment stage, and may be either physiologically or physically based, with physical limitations pertaining to substrata such as rocks or logs where it is difficult for propagules to attach. Substratum specificity of D. johansenii was quantified through logistic regression analysis of log characteristics. Didymodon johansenii has a higher probability of occurrence on logs with a silt layer, high bryophyte species richness (at least four other species) and in later decay stages. Logs with this combination of characteristics were termed 'suitable'. 'Unsuitable' logs are those in early decay stages, with no silt layer and low species diversity. W hen environmental variables were compared for these log groups, suitable and unsuitable logs did not differ significantly in terms of ambient light or temperature, but water stress was lower on suitable logs. Regeneration from gametophyte fragments was tested on suitable and unsuitable logs in plots with and without a silt layer. Silt was the most important factor influencing the number of fragments alive at the end of the study. Sprouting of fragments was positively related to both the silt application and the number of fragments alive at collection time. Differences in environmental conditions on logs did not relate to fragment performance. In Alberta, the relationship between D. johansenii's rarity and substratum limitation is confirmed and the mechanism for this restriction is principally physical adherence of fragments to substratum during establishment. Silt also may have long-term effects on the moss species' growth rate that could relate to its competitive and reproductive capabilities.
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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 | 0.001 |
| 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 | 0.001 |
| 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".