Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:04:25.387546Z
Paper Citation Record · LEDGER
As of 23 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 1 inbound Pith citation observation for arXiv:2411.10074.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T20:04:25.387546Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
80 of 80 outbound references displayed
External citation measurements
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Observation b1411f86-2ffd-4b95-9ccd-ba8c70a22814 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Deep learning as a tool for ecology and evolution,
Reference 1
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Observation 440e7ea9-20ef-445f-96b3-c0ef3dee8d01 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Application of deep learning in ecological resource research: Theories, methods, and challenges,
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Observation d461f8d8-813d-4bb2-a56b-4a39239ecfe8 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Applications for deep learning in ecology,
Reference 3
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Observation b54ea734-273e-41ed-a985-e48a231c8479 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Going deeper in the automated identification of herbarium specimens,
Reference 4
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Observation 229f690c-53a7-4475-bb14-1aac16adc5f0 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Leafnet: A computer vision system for automatic plant species identification,
Reference 5
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Observation fc6cb34c-2afe-402f-a265-be71057887d9 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras,
Reference 6
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis,
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Observation b24a564b-bd6b-45d3-9ee3-595bea2e717a · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning,
Reference 8
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Observation 7c51d4fd-3d17-4365-b411-86e9c04615f7 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,
Reference 9
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Observation 4d46b73b-8420-4081-a9a5-5a05f6f1d915 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process A deep active learning system for species identification and counting in camera trap images,
Reference 10
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Observation 0bb41dec-6c74-4fba-8595-8663741a5133 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Robust ecological analysis of camera trap data labelled by a machine learning model,
Reference 11
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Observation 0e518f4e-340e-4c98-922f-6ca6de951d64 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process A first step towards automated species recognition from camera trap images of mammals using ai in a european temperate forest,
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Observation e46818f9-0ecb-4a5d-a843-62ca8340437b · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
Reference 13
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Observation dfc8c6af-44ac-45ad-83f1-7d877a45b0e5 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Digitization and the future of natural history collections,
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Observation f619a7e5-1c46-4cb3-aa34-3979ea72c461 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process GBIF: Global Biodiversity Information Facility,
Reference 15
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process idigbio as a resource for the digitization of a billion biodiversity research specimens,
Reference 16
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Observation d2bcdc6c-ea9a-4ad8-851f-d41d1df68db3 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Phenology models using herbarium specimens are only slightly improved by using finer-scale stages of reproduction,
Reference 17
Source-reported events for the cited work
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Observation 83fb62d8-7941-4514-9051-8ed49086314c · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Maximizing human effort for analyzing scientific images: A case study using digitized herbarium sheets,
Reference 18
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Observation 7beb836a-c7e9-4805-82c0-99dcd02aeeb8 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process On optimum recognition error and reject tradeoff,
Reference 19
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Observation 7a290b9b-8175-44da-894d-7cf5cf9b87cb · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Accuracy-rejection curves (arcs) for comparing classification methods with a reject option,
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Ad- dressing failure prediction by learning model confidence,
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Observation dd5d792e-708d-4e2e-8995-c5afa73d9e8e · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process To trust or not to trust a classifier,
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Observation 641dfd79-5657-4d7e-8dd4-45f9892b2007 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Selective classification for deep neural networks,
Reference 23
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Observation dcc5f997-9607-4af1-9383-1d6527e3677e · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Macrophenology: insights into the broad-scale patterns, drivers, and consequences of phenology,
Reference 24
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Observation 4d3d588a-1497-4645-bd56-3639847efcfe · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium specimens demonstrate earlier flowering times in response to warming in boston,
Reference 25
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Observation 9b4d44ca-6ac4-4e08-9840-a2eeeb8c628d · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Global warming and flowering times in thoreau’s concord: a community perspective,
Reference 26
Source-reported events for the cited work
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Observation 7b25e186-c182-4cb2-a072-313a26888b77 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Record-breaking early flowering in the eastern united states,
Reference 27
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Observation ed6f215b-a308-401b-bb08-8c81463aae77 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Climate change and flowering phenology in worcester county, massachusetts,
Reference 28
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Observation 6d408d4e-cff9-43ca-b8f8-60abf9911b81 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Climate change and flowering phenology in franklin county, massachusetts1,
Reference 29
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Observation 447af45a-3dad-4067-99d1-f211f89022b8 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Phenological response to climate change in china: a meta-analysis,
Reference 30
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Observation 886011a7-9887-4f5a-849f-d1ee0609c51f · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Fingerprints of global warming on wild animals and plants,
Reference 31
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Observation 04070e3a-b832-4293-94db-b73923c55100 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Favorable climate change response explains non-native species’ success in thoreau’s woods,
Reference 32
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Observation e74de249-5481-4126-8f3c-4f1c5e3c2144 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium specimens reveal the footprint of climate change on flowering trends across north-central north america,
Reference 33
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Observation 0b304b4d-e298-4361-af2b-4c0238146d5d · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium specimens show patterns of fruiting phenology in native and invasive plant species across new england,
Reference 34
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Bbn network for inaturalist competition,
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Photographs and herbarium specimens as tools to document phenological changes in response to global warming,
Reference 36
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium specimens, photographs, and field observations show philadelphia area plants are responding to climate change,
Reference 37
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Observation 1c6e8c30-409d-4584-89e1-40481f00815c · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species’ phenological cueing mechanisms,
Reference 38
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Observation 5a1782cb-8566-4a35-8597-796788d4373b · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Old plants, new tricks: Phenological research using herbarium specimens,
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Observation 1fc6cdec-3730-4acf-acc1-6e9a92b5f479 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium specimens provide reliable estimates of phenological responses to climate at unparalleled taxonomic and spatiotemporal scales,
Reference 40
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Observation fdadfc20-33ba-4cdc-a9b5-153561a4fa05 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Plasticity and not adaptation is the primary 15 source of temperature-mediated variation in flowering phenology in north america,
Reference 41
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium records provide reliable phenology estimates in the understudied tropics,
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Observation b57024b7-b250-412c-85ba-42428b3804ae · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Phylogenetic conservatism in plant phenology,
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Observation 809aa03d-bae1-4941-b3d0-0f5139f46dc5 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Leaf out times of temperate woody plants are related to phylogeny, deciduousness, growth habit and wood anatomy,
Reference 44
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Observation 77c6613d-dd93-4ced-b3b8-9639489febd6 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Back to the future for plant phenology research,
Reference 45
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Observation 56d229c7-56a7-4f83-af66-d4cf3eda0753 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium specimens reveal substantial and unexpected variation in phenological sensitivity across the eastern united states,
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Observation acfcea19-3c0b-4564-9759-d30306a9b146 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Advances in flowering phenology across the northern hemisphere are explained by functional traits,
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Observation 66bbb1a8-ad61-49d8-931a-dd1666cb1206 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Linkage between species traits and plant phenology in an alpine meadow,
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Observation cf2292ad-13ce-4305-8fec-b06c71eb1f78 · outbound
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Observation ba739d81-85b7-4fb6-b1ed-4714fd5e175c · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process The influence of climate warming on flowering phenology in relation to historical annual and seasonal temperatures and plant functional traits,
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Observation 44fcf3cb-0477-40c9-90a4-31a30ce9936d · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Siberian plants shift their phenology in response to climate change,
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Observation ece712f5-d977-4433-ba65-2a3667521475 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Spring flowering response to climate change between 1936 and 2006 in alberta, canada,
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Observation aba26ef7-b710-4ad0-a017-7e48547c0362 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Shifts in flowering phenology in response to spring temperatures in eastern tennessee,
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Observation 1ffa380e-4bd0-4576-b283-29b71d546667 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Divergent responses to spring and winter warming drive community level flowering trends,
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Observation 7a1fd117-5623-48ed-962d-f390644a62db · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Spring-and fall-flowering species show diverging phenological responses to climate in the southeast usa,
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Source-reported events for the cited work
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Observation ee18c977-547f-45b7-9b04-951185f06187 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Temporal variations in frost-free season in the united states: 1895– 2000,
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Observation dd9ccc1e-d974-4f78-b074-de94174743df · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Global warming and the disruption of plant–pollinator interactions,
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Observation 6732650e-3751-4f8a-871f-f4101d7a6037 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Effects of experimental shifts in flowering phenology on plant–pollinator interactions,
Reference 58
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Observation e2dced02-f2ce-4ed5-93a3-f2bf2af15e33 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function,
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Observation 9e949612-4705-4508-841f-93f02e112579 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Phenological shifts alter the seasonal structure of pollinator assemblages in europe,
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Observation 5e90553c-96f9-4847-96f9-3bf8e72b1fa6 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Plant phenology shifts and their ecological and climatic consequences,
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Observation 67ebecd1-0c2c-4f3b-9c50-35a4ffcdc2b7 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Doubtful pathways to cold tolerance in plants,
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Observation 4ded1efd-ba08-4797-9f13-f72dabe9c865 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Model clades are vital for comparative biology, and ascertainment bias is not a problem in practice: a response to beaulieu and o’meara (2018),
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Observation 4478d066-8e85-4a75-b06d-8f758ce6b5b3 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Widespread sampling biases in herbaria revealed from large-scale digitization,
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Observation 6da0a76c-9308-4ec6-8a3c-d0d5131241c0 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Biolog- ical collections for understanding biodiversity in the anthropocene,
Reference 65
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Observation 1263646b-43a9-410d-ba78-3cc4060302d4 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Herbarium data: Global biodiversity and societal botanical needs for novel research,
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Observation cc72d50f-2703-4675-8d07-2e028a771d11 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process The new england vascular plants project: 295,000 specimens and counting,
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Observation c87946d5-61ad-46a1-a5af-28a54afaa13b · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Xception: Deep learning with depthwise separable convolutions,
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Observation 1294774a-896b-402c-a6f7-65943458e6c1 · outbound
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process Visualizing data using t-sne
Reference 69
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