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Record W7015875840

Use of a single epiphyte lichen species Hypogymnia physodes as an indicator of air quality in northern Ontario / by Helmut N. Pfeiffer

2017· other· en· W7015875840 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsLichenBayThunderEpiphyteAir pollutionPollution
DOInot available

Abstract

fetched live from OpenAlex

It has been determined that lichens are excellent indicators of regional
\nair quality, since they are long-lived and totally dependent on atmospheric
\nsources for nutrients. In the summer of 1987, samples of the epiphytic
\nlichen, Hypogymnia physodes (L.) Nyl. were collected from 46 sites in
\nnorthwestern Ontario, Twenty-eight of these sites were sampled around
\nthe city of Thunder Bay, Samples were also collected from 6 sites around
\neach of the northwestern Ontario communities of Kenora, Ignace and Wawa,
\nMorphological observations of the lichens were made before sampling.
\nChemical analyses were carried out and levels of A1, As, Cd, Cu, Fe, Hg,
\nMg, Pb, Sand Zn were determined for each sample by atomic emission
\nspectrometry.
\nOverall levels of elements were low in relation to levels reported in
\nother literature. The ranges of concentrations (ppm) of elements in H.
\nphysodes sampled around Thunder Bay were as follows: Al: 185 - 706;
\nAs: 0,9 -7,1; Cd: 0,2 - 1,2; Cu: 0,8 - 6,9; Fe: 114 - 691; Hg: 0,6 - 5,8;
\nMg: 69 - 393; Pb: 3,9 - 48; S: 42 - 434; Zn: 7 - 92, The area to the south
\nand southwest of Thunder Bay had the highest levels of most elements.
\nThe area to the west had the lowest levels. The Kenora, Ignace and Wawa
\nareas had low levels of most elements relative to Thunder Bay results.
\nLevels of contaminants indicated an inverse relationship existed
\nbetween levels of pollutants and the distance from the pollution source.
\nA number of morphological observations correlated significantly with
\nlevels of certain elements, suggesting possible indicator value.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.065
GPT teacher head0.269
Teacher spread0.204 · 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.

Study designObservational
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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