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Record W6902309702 · doi:10.6084/m9.figshare.28078100

Sea lice infestation dataset for wild and farmed salmon populations on the Pacific coast of Canada (2001-2023)

2025· dataset· en· W6902309702 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typedataset
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsLouseAbundance (ecology)Sampling (signal processing)InfestationLepeophtheirusFish <Actinopterygii>

Abstract

fetched live from OpenAlex

There are six files associated with this dataset around farmed and wild salmon in British Columbia, Canada covering data from 2001 to 2023. Two of the files relate to sea lice observations on wild fish and four relate to sampling for sea lice abundance on Atlantic salmon farms. [all_wild_sample_events.csv] – Description of data fields associated with each wild sampling event; each row represents a field sampling event and includes the date and location of each observation and the observation programme (“Source”) under which each event was carried out. [all_wild_fish_lice.csv] – Description of data fields associated with each wild fish that was assessed for sea louse infestation; each row representing a single fish and in addition to the 12 fields that are used to record sea lice presence according to various species and stages of life cycle development, the host species and physical characteristics are noted, as well as a reference field (“Event_ID”) to link each wild fish record to the field event during which it was sampled. [industry_farm_details] - Description of data fields associated with each Atlantic salmon farm operating on the BC coast; each row representing a different farm from which sea lice observations have been included and includes the farm’s name, location and the aquaculture company responsible for that site. [industry_farm_abundance] - Description of data fields associated with the mean monthly sea lice abundance estimates reported from each farm; each row represents a single monthly farm record and includes the sampling year and month, together with four columns providing mean sea louse abundance values and the number of sampled fish from which these means were generated, as well as a field indicating the ‘weighting’ that should be given to this monthly value when estimating zonal averages, based on the proportional number of fish present on that farm compared to the whole zone. [industry_zone_loads_median] - Description of data fields associated with the estimated median monthly sea lice ‘load’ associated with each DFO fish health zone. Each row contains information on the zone, sampling year and month, with the four sea lice columns representing the median total load of each species/stage recorded, where each farm’s load is estimated based on the monthly mean abundance on that farm multiplied by the estimated number of fish present on that farm during the month under consideration. [DFO_farm_abundance] - Description of data fields associated with mean sea lice abundance estimates reported to DFO by BC operators; each row represents a single farm record and includes the year, month, and typically day of sampling, together with four columns providing mean sea louse abundance values and the number of sampled fish from which these means were generated. (This is very similar to data in the [industry- _farm_abundance] table, but provides average values over a more highly resolved time-frame. Hpwever, these data are only available from 2011 and do not contain any values to allow for the generation of weighted means.)

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.011
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.012

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 head0.044
GPT teacher head0.331
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2025
Admission routes1
Has abstractyes

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