Alpine Ecology in the Iberian Peninsula: What Do We Know, and What Do We Need to Learn?
Bibliographic record
Abstract
The 11th Conference of the Spanish Association of Terrestrial Ecology, held in Pamplona, Spain, on 6–10 May 2013, included a symposium on alpine ecological research in the Iberian Peninsula. This session offered an excellent opportunity to assess the state and progress of alpine ecology in this region, identify knowledge gaps, and discuss further directions for research. Iberian alpine ecosystems are biodiversity hotspots and have traditionally contributed to sustaining rural livelihoods. Today, these ecosystems are subjected to large changes in land uses, including land abandonment, and are affected by climate change. This article reviews the current state of Iberian alpine ecology and proposes a research agenda. Alpine ecology in the Iberian Peninsula is a growing field of research. The need for larger spatial and temporal scales in research and monitoring, along with the integration of socioecological aspects, is a critical issue for understanding the major drivers of change in the alpine ecosystems of the Iberian Peninsula. The implementation of effective mitigation strategies aimed at reducing the impact of the pressing environmental and socioeconomic problems of Iberian mountain areas can only be accomplished through a multidisciplinary and integrative approach.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".