Relationship between vegetation and soil seed banks in an arctic coastal marsh
Bibliographic record
Abstract
Summary The effects of habitat degradation on the soil seed bank at La Pérouse Bay, Manitoba are described. Foraging by lesser snow geese leads to loss of vegetation, coupled with changes in soil abiotic conditions and an increase in salinity. The density of seeds and the relative abundance in the seed bank of species characteristic of undisturbed sites decrease following degradation, while the relative abundance of invasive species increases. Vegetation loss had the greatest impact on seed banks of stress‐tolerant species and the least impact on species with many widely dispersed seeds. The above‐ground vegetation and below‐ground seed bank were less similar in undamaged plots than in disturbed plots. In spite of the low degree of similarity, redundancy analysis of the data indicated that approximately half of the variation in the soil seed bank could be explained by the vegetation data and vice versa. More recently degraded soils had richer soil seed banks than those from older disturbances. Site‐specific factors not only influenced the species present but also the time lag between loss of vegetation and loss of the seed bank. Seed banks in these impacted and fragmented sites do not recover quickly. Seed banks in sandy beach‐ridges were less affected by degradation due to the greater proportion of ruderals present in the original vegetation and the absence of the high soil salinities that are characteristic of degraded salt‐marsh soils.
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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 | 0.000 |
| 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".