Perspectives in Ornithology: Application of tracking and data-logging technology in research and conservation of seabirds
Bibliographic record
Abstract
Seabirds are the most conspicuous and mobile of all pelagic marine organisms.Because most species breed colonially, researchers can study statistically large samples with relative ease.These attributes have long made seabirds valuable for interpreting conditions in the surrounding oceans (Furness and Camphuysen 1997, Boyd et al. 2006, Piatt et al. 2007).Until recently, such studies were usually based on data obtained at breeding colonies or from vessels, but in the past two decades, advances in electronic technology have greatly changed the way we study seabirds, providing unprecedented insights into their locomotion, physiology, foraging behavior, migration, demographics, and exposure to anthropogenic risks at sea.In oceans that are rapidly changing as a result of human activities and global climate change, this information from tagged birds is timely and essential for developing conservation and management strategies for such wide-ranging organisms.In addition, seabirds are increasingly being viewed as tools for oceanography and climatology-capable of providing essential physical and biological information on the sea itself.Here, we highlight some of the exciting new techniques and data that are emerging, discuss some current and future applications, illustrate the roles that seabirds might play in monitoring this watery planet, and discuss the application of new technology in the conservation and management of seabirds.We focus here on La Grangian approaches, concerned with a sequence of data values at points occupied by an individual organism (Schneider 1994), in contrast to studies of populations or communities made at colonies or from vessels or aircraft. AdvAncing TechnologyThe burgeoning market for consumer electronics and communication (e.g., satellite and cell-phone communication) is partly responsible for the advances and miniaturization of sensors, memory storage, and batteries that are revolutionizing marine ornithology (see reviews in Wilson et al. 2002a, Ropert-Coudert andWilson 2005).We review some recent developments, focusing on devices that tell us where birds go (satellite tracking, geolocators, global positioning system [GPS] loggers, and depth recorders) and what they are doing (sensors coupled with data loggers).Tracking devices.-Before1990, conventional VHF radio tags were used to monitor colony attendance and near-colony foraging ".� .�.�.� .�.�.�.� a a a a .�ad d d d dv v v v van an an an anc c c c ce e e e e�� �� �� �� �� i i i i in n n n n e e e e e e e e e ec c c c ctr tr tr tr tron on on on oni i i i ic c c c c techno ogy have great y changed the way we ��tudy ��eabird��, providing unprecedented in��ight�� into their ocomotion, phy��io ogy, foraging behavior, migration, demographic��, and expo��ure to anthropogenic ri��k�� at ��ea.� "
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".