Correction: A Holocene fire history from Terra Nova National Park, Newfoundland, Canada: vegetation and climate change both influenced the fire regime
Bibliographic record
Abstract
Correction on: Lake NF, Arsenault A and Cwynar LC (2024) A Holocene fire history from Terra Nova National Park, Newfoundland, Canada: vegetation and climate change both influenced the fire regime. Front. Ecol. Evol. 12:1419121. doi: 10.3389/fevo.2024.1419121 In the published article, there was an error in [Figure 5] as published. [An error was made when calculating pollen accumulation rate (PAR). The R package "rbacon" was used to create the agedepth model and extract the sediment accumulation rate or deposition time. By default, the argument "cmyr" is set to FALSE and sediment accumulation rates are calculated in years per cm. To obtain the sediment accumulation rate, the cmyr argument must be set to TRUE. Due to the wording in the manual, we thought that the sediment accumulation rate was extracted, but deposition time was extracted. In the paper, we used the incorrect formula to calculate PAR given that we extracted the deposition time and not the sediment accumulation rate. In the correction, we used □□□□□□ = The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.In the published article, there was an error in [Figure 6] as published. [Several pollen concentration values were incorrect due to the volume not being standardized to 1 cm 3 . An error was also made when calculating pollen accumulation rate (PAR). The R package "rbacon" was used to create the age-depth model and extract the sediment accumulation rate or deposition time. By default, the argument "cmyr" is set to FALSE and sediment accumulation rates are calculated in years per cm. To obtain the sediment accumulation rate, the cmyr argument must be set to TRUE. Due to the wording in the manual, we thought that the sediment accumulation rate was extracted, but deposition time was extracted. In the paper, we used the incorrect formula to calculate PAR given that we extracted the deposition time and not the sediment accumulation rate. In the correction, we used □□□□□□ = The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.In the published article, there was an error. [Several pollen concentration values were incorrect due to the volume not being standardized to 1 cm 3 . An error was also made when calculating pollen accumulation rate (PAR). The R package "rbacon" was used to create the age-depth model and extract the sediment accumulation rate or deposition time. By default, the argument "cmyr" is set to FALSE and sediment accumulation rates are calculated in years per cm. To obtain the sediment accumulation rate, the cmyr argument must be set to TRUE. Due to the wording in the manual, we thought that the sediment accumulation rate was extracted, but deposition time was extracted. In the paper, we used the incorrect formula to calculate PAR given that we extracted the deposition time and not the sediment accumulation rate. In the correction, we used □□□□□□ = "[The fire-facilitated Picea was also likely in low density stands for much of zone 2, as the average pollen influx of ~1,750 grains/cm 2 / year was lower than the average Picea influx of pollen zone 4b where a closed canopy was likely present (~3,100 grains/cm 2 /year) (Figure 6B).]"The corrected sentence appears below:"[The fire-facilitated Picea was also likely in low density stands for much of zone 2, as the average pollen influx of ~1,900 grains/cm 2 / year was lower than the average Picea influx of pollen zone 4b where a closed canopy was likely present (~4,800 grains/cm 2 /year) (Figure 6B)]"The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.In the published article, there was an error. [Permitting information was not included in the acknowledgments].A correction has been made to [Acknowledgments], [Paragraph Number 1]. This sentence previously stated:"[We would like to thank K. Costanza, D. Łucówand J.M. St-Jacques for constructive comments used to improve the manuscript. We would also like to thank R. Skinner and K. Gaudet for their aid in the field and lab. The contents of this manuscript build upon the thesis of NL.]"The corrected sentence appears below: "[A.A. as the principal investigator received a Parks Canada Research and Collection Permit to sample within Terra Nova National Park (Permit Number: TNP-2017-25138). We would like to thank K. Costanza, D. Łucówand J.M. St-Jacques for constructive comments used to improve the manuscript. We would also like to thank R. Skinner and K. Gaudet for their aid in the field and lab. The contents of this manuscript build upon the thesis of NL.]"The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.146 | 0.057 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".