Bibliographic record
Abstract
Abstract Conservation planning requires extensive amounts of data, yet data collection is expensive, and there is often a trade‐off between the quantity and quality of data that can be collected. Researchers are increasingly turning to community science programs to meet their biodiversity data needs, yet the reliability of such data sources is still a common source of debate. Here, we argue that professionally collected data are subject to many of the limitations and biases present in community science datasets. We explore four common criticisms of community science data, and comparable issues that exist in data collected by experts: spatial biases, observer variability, taxonomic biases and the misapplication of data. We then outline solutions to these problems that have been developed to make better use of community science data, but can (and should) be equally applied to both kinds of data. We highlight four main solutions based on research using community science data that can be applied across all biodiversity data collection and research. Statistical techniques that have been developed for processing community science data can equally help account for spatial biases and observer variation in professional datasets. Benchmarking or vetting one dataset against another can strengthen evidence and uncover unknown sources of biases. Professional and community science datasets can be used together to fill knowledge gaps that are unique to each. Careful study design that accounts for the collection of relevant and important covariate data can help statistically account for sources of bias. Currently, a double standard exists in how researchers view data collected by professionals versus those collected by community scientists. Our aim is to ensure that valuable community science data are given the prominent place they deserve, and that data collected by experts are appropriately vetted and biases accounted for using all the tools at our disposal.
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 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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".