Long-term ecological vegetation data for Carrizo National Monument, California
Bibliographic record
Abstract
Vegetation dynamics were censused at peak flowering in March to April from 2015 to 2018 at Carrizo National Monument. A total of 4 distinct sites were used to monitor vegetation. The shrub species Ephedra californica was used as the foundation species to structure shrub-open contrasts annuals. A paired shrub-open contrast using 0.5 x 0.5m quadrats were deployed under the shrub canopy and at least 1m at open microsites defined as adjacent sites without canopy cover by a dominant plant species. A total of 1194 independent shrubs were used (and the same number of open microsites), and repeated-measures were avoided by randomly selecting sites at each site each year using transects. For each shrub, the height, longest dimension in width, and perpendicular width was measured, and the volume for a sphere was used to summarize shrub sizes in subsequent analyses. The total abundance of each annual species present was recorded within plots. All analyses to support these data including quality assessment and control were done in R version 3.5.1 and published at Zenodo: Lortie, C.J. 2018. Carrizo long-term ecological data analyses. Zenodo: DOI: 10.5281/zenodo.1455808.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".