Bibliographic record
Abstract
“Hold still, you've got something on your nose!” There is only one stigma (that of plants, the pollen-receiving organ!) associated with having food on your face, at least where pollen and island-dwelling lizards are concerned. Lizards are important pollinators, in some cases replacing birds and insects in many island systems. This ornate day gecko (Phelsuma ornata) on Round Island, Mauritius, hopped from plant to plant, enthusiastically feeding on the nectar of endemic plants, and transporting pollen in the process. In fact, it was not unusual to see pollen grains sprinkled across the face of these attractive geckos during feeding forays. Just out of reach of a lapping tongue, the settled pollen grains were in an ideal position for transfer to the stigma of the next plant(s). Lizards play a seemingly sizable, yet largely underappreciated and understudied, role in pollination in island ecosystems. Round Island, the last remnant of endemic Mauritian lowland palm forest, has been saved following the eradication of non-native herbivores and intensive replanting of endemic plants. However, island restoration has been dogged by the persistence of exotic plants. Do these pollinating lizards influence the interplay of native and exotic plants? How large is the role of this little gecko in reconnecting and strengthening ecological connections in this community? Plant–animal interactions, such as pollination, have broad ecological and conservation implications, especially on islands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".