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Record W4390263493 · doi:10.47193/mafis.37x2024010112

Characterizing oceanographic conditions near Coiba Island and Pacific Panama using 20 years of satellite-based wind stress, SST and chlorophyll-a measurements

2023· article· en· W4390263493 on OpenAlexafffund
G. B. Crawford, Matthew Mepstead, Edgardo Díaz‐Ferguson

Bibliographic record

VenueMarine and Fishery Sciences (MAFIS) · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsOntario Tech University
FundersUniversity of Ontario Institute of Technology
KeywordsPanamaOceanographyWind stressClimatologySea surface temperatureSubmarine pipelinePelagic zoneEnvironmental sciencePeninsulaGeographyGeologyEcology

Abstract

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Coiba Island and the associated Special Zone of Marine Protection represent an important, yet poorly studied marine reserve along the Pacific coast of Panama. While efforts have recently began to establish monitoring programs in the region, a range of historical, marine-related environmental measurements already exist, derived from satellite-based observations. The goal of this paper was to use long-term datasets for key variables to provide qualitative insights (i.e. descriptive oceanography) of climatological conditions and interannual variability in the Pacific Panama region. These are underpinned with numerical assessments, providing an important baseline for ongoing and future studies, particularly in the Coiba Island/Gulf of Chiriqui region. In particular, we examined 20 years (January 2003-December 2022) of wind stress, sea surface temperature (SST), and chlorophyll-a (Chl-a), spanning the neritic and pelagic regions of the Pacific Panama coast. During the dry season (northern winter), the well-known, seasonal, regional Panama wind jet appeared across the Gulf of Panama, leading to surface mixing and SST cooling that eventually extended across most of the Panama Bight. West of the Azuero Peninsula, SST increased and surface warming extended further offshore from January through April. The SST in the Gulf of Chiriqui during this period was about 1 °C warmer on average than east of Coiba Island. By July and August, offshore SST gradients became largely longitudinal, cooling occured across the season, and the SST on either side of Coiba Island was nearly the same. The influence of the Panama jet in the Gulf of Panama was clear in the Chl-a data as well, with upwelling-driven values peaking in February/March (up to 11 mg m-3, with a monthly climatological value of around 2 mg m-3 during this period). During the rest of the year, the Chl-a concentration in this region averaged around 0.5-1.0 mg m-3. In the Gulf of Chiriqui and the region east of Coiba Island, the climatological monthly averages were roughly 0.3-0.5 mg m-3 and 0.4-0.6 mg m-3, respectively. Somewhat surprisingly, very high Chl-a values were present in the satellite data for the Gulf of Chiriqui during May 2007 and June 2008, peaking at 16 mg m-3 and 32 mg m-3 at a location just west of Coiba Island, respectively. It remains unclear as to the cause of these apparent blooms. Even when the high Chl-a values were excluded in the calculation of climatological averages in the Gulf of Chiriqui, however, there is a suggestion of modest seasonality in Chl-a values, with slightly elevated values (~ 0.4 mg m-3) peaking around May and October. During the extreme El Niño event of 2015-2016, the monthly-averaged SST along the Panama Pacific coast was warmer than average, with elevated levels of up to + 2 °C and lasting 12 months in the Gulf of Chiriqui. In the Gulf of Panama, the monthly-averaged SST anomalies were up to + 1.7 °C, although the temperatures returned to near-seasonal averages after roughly 5 months.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.214
Teacher spread0.184 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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