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Record W2026619828 · doi:10.1179/174963107x226435

Neolithic cod (<i>Gadus morhua</i>) and herring (<i>Clupea harengus</i>) fisheries in the Baltic Sea, in the light of fine-mesh sieving: a comparative study of subfossil fishbone from the late Stone Age sites at Ajvide, Gotland, Sweden and Jettböle, Åland, Finland

2007· article· en· W2026619828 on OpenAlexfundno aff
Carina Olson, Yvonne Walther

Bibliographic record

VenueEnvironmental Archaeology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersAlberta Agricultural Research Institute
KeywordsHerringClupeaGadusFisheryArchipelagoBaltic seaFishingGeographyFish <Actinopterygii>BiologyArchaeologyOceanographyGeology

Abstract

fetched live from OpenAlex

During the Late Stone Age, the sites of Ajvide and Jettböle were located on the seashore but in quite different marine environments. Ajvide on Gotland had direct access to the open sea of the central Baltic, while Jettböle in the Åland archipelago was surrounded by islands and skerries in the northern part of the Baltic Sea. Continuous excavations at Ajvide revealed large amounts of Cod (Gadus morhua) while herring (Clupea harengus) was found in small numbers. At Jettböle, as well, cod bones have been observed in large numbers while the skeletal remains of herring were few. In this study, soil samples of fishbone materials from Ajvide and Jettböle were sieved through screens of different mesh-sizes and then osteologically analysed. The finer screens aided the recovery of small herring bones that usually are lost when sieving through a common standard mesh-size of 4 mm. The results of the study confirmed the importance of fine-mesh sieving for the retrieval of the fishbone materials. Additionally, the achieved osteometric data indicated a difference in cod and herring size between the sites. Other factors that form our base for the understanding of Neolithic fishing strategies are: a general knowledge of the behaviour of the retrieved fish species, a reconstruction of the ancient marine environment and the abundance of fish species at each site.

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 score0.136
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.016
GPT teacher head0.238
Teacher spread0.221 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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