Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring
Bibliographic record
Abstract
Aim: Effective management strategies for conserving biodiversity and mitigating the impacts of global change rely on access to comprehensive and up-to-date biodiversity data. However, manual search, retrieval, evaluation, and integration of this information into databases present a significant challenge to keeping pace with the rapid influx of large amounts of data, hindering its utility in contemporary decision-making processes. Automating these tasks through advanced algorithms holds immense potential to revolutionize biodiversity monitoring. Innovation: In this study, we investigate the potential for automating the retrieval and evaluation of biodiversity data from Dryad and Zenodo repositories. We have designed an evaluation system based on various criteria, including the type of data provided and its spatio-temporal range, and applied it to manually assess the relevance for biodiversity monitoring of datasets retrieved through an application programming interface (API). We evaluated a supervised classification to identify potentially relevant datasets and investigate the feasibility of automatically ranking the relevance. Additionally, we applied the same appraoch on a scientific literature source, using data from Semantic Scholar for reference. Our evaluation centers on the database utilized by a national biodiversity monitoring system in Quebec, Canada. Main conclusions: We retrieved 89 (55%) relevant datasets for our database, showing the value of automated dataset search in repositories. Additionally, we find that scientific publication sources offer broader temporal coverage and can serve as conduits guiding researchers toward other valuable data sources. Our automated classification system showed moderate performance in detecting relevant datasets (with an F-score up to 0.68) and signs of overfitting, emphasizing the need for further refinement. A key challenge identified in our manual evaluation is the scarcity and uneven distribution of metadata in the texts, especially pertaining to spatial and temporal extents. Our evaluative framework, based on predefined criteria, can be adopted by automated algorithms for streamlined prioritization, and we make our manually evaluated data publicly available, serving as a benchmark for improving classification techniques.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".