Minimizing Data Waste: Conservation in the Big Data Era
Bibliographic record
Abstract
Session motivation and objectivesAdvances in computing, statistics, and technology over the past few decades has resulted in the accumulation of massive amounts of biodiversity data, as well as novel methods for using and integrating them (Miller et al. 2019).Data that have been collected for decades or even centuries can now be analyzed and applied in brand new ways.These data include alternative sources of information such as citizen science (also called community science) programs (Butcher et al. 1990, Sullivan et al. 2014, Hudson et al. 2017).In the midst of a global biodiversity crisis, these databases may hold the key to detecting important obstacles and threats to conservation, as well as determining the best interventions before it is too late.The ecological processes in question frequently occur across broad spatial and temporal scales; migratory species' ranges can span thousands of kilometers, and the impacts of threats such as climate change cannot necessarily be assessed on a local scale.Conventional methods of biodiversity monitoring may therefore be inadequate in the face of broad scale change.The individual researcher can generally only collect data on a localized scale, yet with the proper data practices, this information can contribute to a better understanding of the bigger picture (Sutherland et al. 2009).
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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 | 0.074 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.027 | 0.052 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".