Global Malaise Trap Project and LIFEPLAN Malaise sampling v1
Bibliographic record
Abstract
Lifeplan is a global biodiversity monitoring project with the aim of assessing the current state of biodiversity worldwide, and using this knowledge to generate predictions of how biodiversity might look in the future. In this protocol we describe the materials and method used to sample flying insects with a Malaise trap for the Global Malaise Trap Project and the LIFEPLAN project on a global scale and in a wide variety of environmental conditions and habitats. The aim is to identify species in further analysis (e.g. image recognition, DNA sequencing) and create species lists for different locations across the globe. This protocol contains a detailed description from setup of the Malaise traps in the field to weekly data collection, as well as steps to reduce ethanol in the samples and collect necessary information to help in species identification. We identify the equipment used in Lifeplan, but also give technical specifications of that equipment so that other users of this protocol can find equivalent alternative equipment. We also specify what metadata should be collected with the Malaise trap data. The technical solution we use to collect metadata in the Lifeplan project is described in detail in the full Lifeplan protocol. It is critical that we employ standardized operating procedures for the Malaise trapping. Our coordinated efforts will ensure specimen preservation for sequence analysis and high data quality, permitting the comparison of sites at a global scale. For global standardization with the BIOSCANinitiative, of which LIFEPLAN is a part. LIFEPLAN is based on bulk processing (metabarcoding) of samples and automatic image recognition which are outside of the scope of this protocol and will be described elsewhere.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.022 |
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".