A novel photographic approach for monitoring the structural heterogeneity and diversity of grassland ecosystems
Bibliographic record
Abstract
Studies that investigate the space-filling heterogeneity of biological structures in plant communities remain scarce. The main objective of this study was to evaluate the relationship between newly developed photographic measures of structural heterogeneity in digital images and plant species composition in the context of a long-term grassland experiment. We tested a close-range photographic protocol using measures of structural heterogeneity in gray-tone images, namely mean information gain (MIG) and spatial anisotropy, to assess differences in the compositional (species richness) and functional characteristics (plant height and flowering) of 78 managed grassland communities. We also implemented a random placement model of community assembly to explore the links between our measures of structural complexity and the geometric pattern of plant communities. MIG and spatial anisotropy correlated with the growth and species richness of grassland communities. Simulations showed that structural heterogeneity in gray-tone digital images is a function of the size distribution and orientation pattern of plant modules. This easy, fast and non-destructive methodological approach could eventually serve to monitor the diversity and integrity of various ecosystems at different resolutions across space and time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".