Effect of <i>Phalaris aquatica </i>on the abundance and diversity of vertebrate animals in Mediterranean coastal grasslands in California
Bibliographic record
Abstract
Harding grass (Phalaris aquatica), an invasive non-native species of bunchgrass, has been introduced to grasslands in many regions of California, particularly those with a history of disturbance, such as tilling and grazing. Due do the invasive nature of Harding grass, we sought to examine whether it has an effect on small animal abundance and diversity in the grasslands, Rancho Marino Reserve (RM) and Fiscalini Ranch Preserve (FR) in California. Both grasslands have similar climate and geographic location but differ in management history. Two transects were created in each site, with eight plots per transect. Animal cameras were deployed over the course of three nights to examine the abundance and diversity of small animals. Due to the history of tilling and planting of RM, and its increase in P. aquatica coverage, there was less animal abundance and diversity compared to FR. The results indicated that the untilled/unplanted areas had more animal abundance and diversity compared to tilled/planted due to the lack of Harding Grass. This can be due to factors such as diminished soil quality, difficulty in maneuvering in the tall grass, and adaptability to native vegetative state. Invasive plants have the ability to increase rapidly in space and potentially lead to ecosystem degradation. This adds further knowledge in the relationship between small animals and their habitats and helps conservation biologists ensure mammalian populations remain stable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".