Snail trails: Use of RFID technology to discover how far intertidal snails travel and where they live
Bibliographic record
Abstract
Mark and recapture (MR) is a common technique used to study animal behaviour, however, conventional tagging methods involving direct observation have difficulties in locating small, cryptic animals in complex environments. Radio frequency identification (RFID) technology can solve this problem, to offer a promising advantage in the intertidal zone. This project represents the first use of RFID technology to discover which habitats snails use throughout the summer, how far throughout the intertidal zone they move per day, and how widely they disperse towards other populations. When mounted to snails, RFID technology, which uses small passive integrated transponder (PIT) tags, allows a researcher to detect individual snails even when they are hidden from view. The specific goals of this study were to determine: 1) the effectiveness of using RFID to study snail behaviour, 2) which microhabitats were used most often by the snails, 3) how far the snails travelled each day, and 4) how far the snails dispersed over the summer. This research was conducted on the intertidal snail, Nucella ostrina, near the Bamfield Marine Sciences Center, in British Columbia, Canada. In summer 2015, I attached one 12 mm PIT tag to each of 64 snails and located their position in the intertidal zone daily using an RFID reader. PIT tags had no detectable effect on snail movement or survival; thus, using RFID technology is an effective technique for tagging snails to study their behavior. Intertidal snails occupied a variety of hidden microhabitats throughout the study, which made 30% of them invisible to the human eye without the use of RFID technology. In addition, intertidal snails moved very little each day (13.82 ± 7.01 cm) and moved non-directionally, which led to limited displacement over the study period where majority of snails (78%) only dispersed up to 1 m from their starting position. Consequently, snail populations may have limited gene flow between neighboring populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".