Incorporating measures of data quality into plant-pollinator databases
Bibliographic record
Abstract
The development of large databases of plant-pollinator relationships poses both great opportunities and a particular problem for scientists and practitioners interested in these interactions. A major issue is that it is rare for measures of data quality to be included, in the sense of stating the evidence by which animal X has been determined to be a pollinator of plant Y. Adding such information to databases is vital if we are to fully understand the plant-pollinator relationships that they describe and address information gaps. We present some examples of data quality schemas that have been used in the past and then adopted by the Pollinators of Apocynaceae Database and the Database of Pollinator Interactions (DoPI), and how the forthcoming USDA-NRCS PLANTS database has tackled this question. In addition, we discuss the use of controlled vocabularies developed by the Brazilian Network of Plant-Pollinator Interactions (REBIPP), allied to a vocabulary based on the Darwin Core standard. It is our hope that the pollination ecology community will see the importance of these or other evaluations of data quality and adopt them accordingly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".