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Record W2005616448 · doi:10.1890/es14-00230.1

Modeling and mapping isotopic patterns in the Northwest Atlantic derived from loggerhead sea turtles

2014· article· en· W2005616448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEcosphere · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans CanadaUniversity of Manitoba
FundersNortheast Fisheries Science CenterSouthwest Fisheries Science CenterSoutheast Fisheries Science CenterU.S. Fish and Wildlife ServiceFisheries and Oceans CanadaBureau of Ocean Energy ManagementGoddard Space Flight CenterUniversity of Central FloridaNational Oceanic and Atmospheric AdministrationNorth Carolina Sea Grant, North Carolina State UniversitySea Turtle ConservancyNational Fish and Wildlife FoundationJohns Hopkins UniversityNational Marine Fisheries ServiceU.S. Department of the Interior
KeywordsOceanographySea turtleFisheryGeologyTurtle (robot)EcologyGeographyBiology

Abstract

fetched live from OpenAlex

Stable isotope analysis can be used to infer geospatial linkages of highly migratory species. Identifying foraging grounds of marine organisms from their isotopic signatures is becoming de rigueur as it has been with terrestrial organisms. Sea turtles are being increasingly studied using a combination of satellite telemetry and stable isotope analysis; these studies along with those from other charismatic, highly vagile, and widely distributed species (e.g., tuna, billfish, sharks, dolphins, whales) have the potential to yield large datasets to develop methodologies to decipher migratory pathways in the marine realm. We collected tissue samples (epidermis and red blood cells) for carbon (δ 13 C) and nitrogen (δ 15 N) stable isotope analysis from 214 individual loggerheads ( Caretta caretta ) in the Northwest Atlantic Ocean (NWA). We used discriminant function analysis (DFA) to examine how well δ 13 C and δ 15 N classify loggerhead foraging areas. The DFA model was derived from isotopic signatures of 58 loggerheads equipped with satellite tags to identify foraging locations. We assessed model accuracy with the remaining 156 untracked loggerheads that were captured at their foraging locations. The DFA model correctly identified the foraging ground of 93.0% of individuals with a probability greater than 66.7%. The results of the external validation (1) confirm that assignment models based on tracked loggerheads in the NWA are robust and (2) provide the first independent evidence supporting the use of these models for migratory marine organisms. Additionally, we used these data to generate loggerhead‐specific δ 13 C and δ 15 N isoscapes, the first for a predator in the Atlantic Ocean. We found a latitudinal trend of δ 13 C values with higher values in the southern region (20–25 °N) and a more complex pattern with δ 15 N, with intermediate latitudes (30–35 °N) near large coastal estuaries having higher δ 15 N‐enrichment. These results indicate that this method with further refinement may provide a viable, more spatially‐explicit option for identifying loggerhead foraging grounds.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.016
GPT teacher head0.206
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it