Is there a middle way between permanent plots and chronosequences?
Bibliographic record
Abstract
Although permanent plots have proven critical to studies of vegetation dynamics, their logistic limitations have led to the wide use of chronosequences as an alternative. Here, we test whether or not an approach combining permanent plots and chronosequences could be used successfully to accurately predict the vegetation changes that one would see in permanent plots in the same area. We used plot data from five pastures in the Luquillo Mountains of Puerto Rico, USA, and found that (i) for species composition and abundance, the five pastures did not form a single trajectory or show any convergence, (ii) for successional rate, there was a general decrease with time since abandonment for most pastures, but that decrease was not monotonic and one pasture of the five had no decrease at all, and (iii) total species richness and total plant cover showed pastures that lined up well by age, forming almost a single trajectory with little variation. We conclude that the utility of using chronosequences either alone or with permanent plots depends largely on the parameter under study with broad structural parameters, such as total species richness and total plant cover, performing best.
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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 | 0.057 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".