Succession rates and patterns twelve years after land use abandonment in the estuary of the river aliakmon, n. greece
Bibliographic record
Abstract
Abstract Vegetation succession is a key element for research studying biodiversity losses, effects of climatic change on ecosystems, invasive species and restoration of ecosystems in which human activities have shifted their natural or semi-natural vegetation. Surrogate variables like Pignatti's bioindicator values or dissimilarity indices can provide further insights regarding succession trajectories aggregating the combined effects of changes in the cover/abundance of taxa. The land-use abandonment in an area in the estuary of the River Aliakmon, N. Greece, provides an opportunity to study medium-term rates and patterns during the first twelve years of vegetation succession. Cluster and time-series analyses of turnover rates, Sørensen's dissimilarity index and Pignatti's bioindicator values revealed clear differences in succession patterns and rates among permanent plots. Succession rates and patterns in the study area were found to be dominated by two ecological factors. On the one hand, availability of fresh water, assumed by the proximity to the river, allows the fast growing Populus alba to develop a forest canopy that radically alters the shading environment which, in its turn, controls succession trajectories. Increased soil salinity on the other hand, allows salt-tolerant taxa to be quickly established, and defines the species inventory on these sites as early as the first years of succession.
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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 | 0.001 | 0.001 |
| 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".