MétaCan
Menu
Back to cohort
Record W7095886529

Smallbodied fishes of Tar Creek and other small streams

2006· article· en· W7095886529 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSTREAMStar (computing)HabitatFish <Actinopterygii>Hydrology (agriculture)Drainage
DOInot available

Abstract

fetched live from OpenAlex

Fishes of small streams in Ottawa County, Oklahoma, were collected by seine from July 2004 through October 2005 to provide base-line data on species occurrences in Tar Creek and surrounding watersheds. Nine sites within the study area have been receiving mine drainage contaminated with iron, zinc, lead and cadmium for at least three decades fol-lowing the cessation of mining in the Tri-State Mining District of Oklahoma, Kansas, and Missouri in the 1960s. No such known contamination exists for the remaining sampling sites which serve as “controls ” for any future changes in fishes at the polluted sites. Fifty-three collections were made at 10 sites throughout Tar Creek watershed, and 26 collec-tions were made on surrounding watersheds at sites on Coal, Cow, Little Elm, Hudson, Mud, and Four-mile creeks in Ottawa County. A total of 34 species and hybrid sunfish representing ten families were collected. Faunal composition of impacted streams was compared with non-impacted streams. Species richness, corrected for habitat size, was significantly lower in impacted sites than in non-impacted sites. As planned treatment systems for the mine drainage are implemented, the information in this report will allow evaluation of any changes in fish communities. © 2006 Oklahoma Academy of Science.

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)0.000
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 score0.197
Threshold uncertainty score0.998

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)0.0010.000

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 head0.039
GPT teacher head0.198
Teacher spread0.159 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicWater Quality and Resources StudiesFrench-language works237,207