Use of High-Frequency Noninvasive Electromagnetic Biosensors to Detect Ocean Acidification Effects on Shellfish Behavior
Bibliographic record
Abstract
Although ocean acidification studies related to marine animal behavior have increased in recent years, the behavioral effects of ocean acidification on shellfish are relatively understudied, even though marine shellfish exhibit a wealth of behaviors that can modify organismal interactions and biological community functioning. Furthermore, detecting acute behavioral changes may provide a biological indicator of ecosystem stress and/or an early warning system for aquaculture operations. This article highlights a new and emerging technology—high-frequency noninvasive (HFNI) electromagnetic biosensors—that can be used to document acute and long-term effects of elevated CO2 on the valve-gaping behavior of marine bivalves. An overview of the technology is presented, and the current and potential uses of these biosensors in ocean acidification research are highlighted, along with current limitations and next steps. Although a handful of studies have used these biosensors to test for effects of acidification on bivalve valve-gaping behavior, their potential for testing critical and novel hypotheses regarding ocean acidification effects in a broader range of shellfish taxa is currently underused. Ultimately, this article provides a basis for expanding ocean acidification research on shellfish behavior through the use of HFNI electromagnetic biosensors.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".